Exploring the emotional intelligence construct: A cross-cultural investigation

Exploring the Emotional Intelligence Construct:

A Cross-Cultural Investigation

Gina Ekermans

Doctor of Philosophy

Brain Sciences Institute

Faculty of Life and Social Sciences

Swinburne University of Technology

June 2009

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TABLE OF CONTENTS

DECLARATION…………………………………………………………………………..vii

ACKNOWLEDGEMENTS……………………………………………………………….viii

ABSTRACT………………………………………………………………………………...ix

LIST OF TABLES……………………………………………………………………….....xi

LIST OF FIGURES…………………………………………………………………….. xviii

Chapter 1: Introduction 1

1.1 Overview 1

1.2 Structure of the dissertation 4

Chapter 2: Emotional Intelligence and Cross-cultural psychology 7

2.1 Overview 7

2.2 Emotional Intelligence (EI) 7

2.2.1 The state of the field 7

2.2.2 On the conceptual coherence of EI 10

2.2.3 Self-report EI inventories: a review of cross-cultural applications 12

Twenty-item Toronto Alexithymia Scale-III (TAS-20) 12

Bar-On Emotional Quotient Inventory (EQ-i) 16

Trait Meta-Mood Scale (TMMS) 18

Trait Emotional Intelligence Questionniare (TEIQue) 19

Schutte Self-Report Inventory (SSRI) 21

Wong and Law Emotional Intelligence Scale (WLEIS) 24

Closing Remarks 25

2.2.4 Moving forward with EI 25

2.3 Cross-cultural psychology and assessment 27

2.3.1 General introduction 27

2.3.2 Culture 30

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2.4 Bias and equivalence: a cross-cultural perspective 36

2.4.1 Introduction 36

2.4.2 Construct bias 37

2.4.3 Method bias 39

2.4.4 Item bias and Differential Item Functioning (DIF) 42

2.5 Conclusion 45

Chapter 3: Emotional Intelligence across cultures: theoretical and methodological

considerations 46

3.1 Overview 46

3.2 A brief review of the current state of cross-cultural EI research 47

3.3 Culture and emotion research: implications for EI 52

3.3.1 Emotional regulation in cultures 54

Individualism / Collectivism 55

Display rules 56

Uncertainty Avoidance 57

Power Distance 58

3.3.2 Emotional expression 59


3.3.3 Emotional recognition (judgment) in self and others 61


3.4 Cross-cultural EI research: methodological issues 63

3.4.1 Convergence of two approaches 63

3.4.2 Reframing of bias and equivalence 65

3.4.3 Applying measurement invariance in cross-cultural EI research 66

3.5 Conclusion 68

Chapter 4: Cultural bias investigation of the SUEIT over various cross-national

samples 70

4.1 Introduction 70

4.2 Research questions and hypotheses 70

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4.3 Data analytic strategies 85

4.3.1 Validity extension and generalisation 85

4.3.2 Tests of MI: Omnibus, Configural, and Metric tests of invariance 85

4.3.3 Investigating method bias 88

4.3.4 Investigating Differential Item Functioning (DIF) 90

4.4 Method 91

4.4.1 Measure: Swinburne University Emotional Intelligence Test (SUEIT,

Palmer & Stough, 2001) 91

4.4.2 The SUEIT measurement models 93

4.4.3 Sampling procedure and data collection 94

4.4.4 Participants 96

Australian sample 96

New Zealand sample 97

United States of America (USA) sample 97

Sri Lanka sample 98

Italian sample 98

South African samples 99

4.4.5 Data analytic procedure 100

Structural Equation Modeling (SEM) fit indices 100

Parameter estimation, variable type and item parcels 102

Missing values: imputation by matching 106

Matching the samples 107

4.5 Results 108

4.5.1 Results: validity extension (loose replication) 108

4.5.1.1 Australian results 109

4.5.1.2 New Zealand results 112

4.5.1.3 USA results 115

4.5.1.4 Italian results 117

4.5.1.5 Sri Lanka results 120

4.5.1.6 South African White results 122

4.5.1.7 South African non-White results 125

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4.5.2 Results: Validity generalisation 128

4.5.3 Discussion: validity extension and generalisation results 128

4.5.4 Results: cross-cultural validation (testing MI) 132

4.5.5 Discussion: cross-cultural validation 137

Construct bias 137

4.5.6 Results: method bias 139

Response styles: ERS and ARS indices 139

Verbal ability / bilingualism of respondents (item keying effects) 141

4.5.7 Discussion: method bias 147

Response styles: ERS and ARS indices 147

Verbal ability / bilingualism of respondents (item keying effects) 153

4.5.8 Results: Differential Item Functioning 155

4.5.8.1 Results: 2-way ANOVA DIF 156

4.5.8.2 Discussion: 2-way ANOVA DIF results 160

4.5.8.3 Results: Multiple-Group Mean and Covariance Structure Analyses

(MACS) DIF 161

Australia and New Zealand 164

Australia and USA 165

Australia and Italy 167

Australia and South Africa White 169

Australia and South Africa Non-White 171

Australia and Sri Lanka 174

4.5.8.4 Discussion: MACS DIF results 176

4.5.9 Results: Latent mean differences in EI 179

4.5.10 Discussion: Latent mean differences in EI 184

4.6 General discussion 186

4.7 Limitations and suggestions for future research 193

4.8 Conclusion 197

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Chapter 5: Final discussion and conclusions 199

5.1 Introduction 199

5.2 Theoretical and practical implications 201

5.3 Future research 207

5.4 Conclusion 211

REFERENCES 212

Appendix 1 261

Appendix 2 263

Appendix 3 277

Appendix 4 284

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DECLARATION

I declare that this thesis does not incorporate any material previously submitted for a degree

in an University, College or Advances Education, or other educational institution; and that

to the best of my knowledge and belief it does not contain any material previously

published or written by another person except where due reference is made in the text.

Gina Ekermans

17 June, 2009

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ACKNOWLEDGEMENTS

This PhD dissertation is a testimony of the goodness of God, Jesus Christ and the Holy

Spirit in my life. He has been faithful at every step of the way. “But there is a spirit in a

man, and the breath of the Almighty gives him understanding” (Job 32:8).

I would like to sincerely thank Prof Con Stough for constant support, encouragement and

guidance throughout this project. Special thanks also to Prof Don Saklofske for coming

onboard and providing valuable support and inputs along the way.

I would like to thank Prof Callie Theron for introducing me to Psychometrics at

undergraduate level (a very thorough introduction!). His unique passion for the subject

grabbed my attention and inspired me to delve deeper into the subject. Many thanks to

Callie for allowing me to always walk into his office (without an appointment) and discuss

SEM / LISREL questions and issues (for hours!). I would also like to thank Prof Ype

Poortinga for reading earlier versions of the draft and providing unique feedback on the

cross-cultural aspects thereof. This made me look at my work from a different angle.

I would also like to thank Con and Dr Ben Palmer for making large portions of the data

used in this dissertation, available to me. Thank you to Luke Downey, Peter Walker, and

Vilfredo De Pascalis for helping to gather data.

Thank you to fellow PhD students at the Brain Sciences Institute for fun times and support

during the completion of this project! Lastly, to all my friends, colleagues (special thanks to

Ronel du Preez) and family, thank you for your continued support and encouragement

(special thanks to Ronel van Rooyen, Erika Luyckx and Johann Görgens)!

Thank you to the Oppenheimer Trust and the National Research Foundation South Africa

for funding to pursue this project.

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ABSTRACT

Test transportability is a prevalent issue in psychological measurement. Oakland (2004)

report that foreign developed tests are used, in most countries, more frequently than

nationally developed tests. The main aim of this research was to differentiate cultural bias

from true construct variance in a self-report measure of Emotional Intelligence (EI) in the

workplace (the Swinburne University Emotional Intelligence Test, SUEIT; Palmer &

Stough, 2001). Such investigations are necessary as tests of EI are increasingly being used

extensively around the world. For example, the Twenty-item Toronto Alexithymia Scale-III

(TAS-20) (Parker, Taylor & Bagby, 2003) has been translated into 18 languages. The Bar-

On Emotional Quotient Inventory (EQ-i, Bar-On, 1997) has 22 language translations and

normative data is available in more than 15 countries (Bar-On, 2000).

This investigation focused on the generalisability and transportability of the SUEIT, a

prominent self-report monocentered (i.e. instrument from a single, Western cultural

background; Van de Vijver & Leung, 2001) EI measure to two Western (USA, New

Zealand) and four non-Western countries (Italy, South Africa White and Non-White, Sri

Lanka). It could be argued that the Western cultural origin of the test (i.e. Australia)

contains descriptions of EI as defined within Australian culture. Cultural dimension

differences (Hofstede, 1980, 2001) could introduce cultural bias into Western EI measures

on various levels, when applied in non-Western environments. On a broad conceptual level

the central research question this study aimed to investigate can be formulated as follows:

to what extent do Hofstede (1980, 2001) cultural dimensions systematically influence the

cross-cultural transportability of a self-report EI measure?

Measurement invariance (configural and metric invariance; VandenBerg & Lance, 2000),

method bias (national differences in response styles, i.e. extreme response styles and

acquiescence; Van Herk, Poortinga, & Verhallen, 2004; as well as negatively keyed method

factors) and the differential item functioning (uniform and non-uniform DIF were

investigated with a series of Mean and Covariance Structures Analyses models run in

LISREL 8.8; Chan, 2000) of the SUEIT over the various samples, were investigated. It

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was argued that the amount of cultural bias would increase as the Cultural Distance (CD,

the extent to which cultures are similar or different; Shenkar, 2001) (Kogut & Singh, 1988)

between a given cultural group (e.g. Sri Lanka) and Australia increase. That is, the more a

particular culture is dissimilar to Australian culture (origin of the SUEIT) the more

pronounced the influence of culture will be on the transportability of the instrument. In

addition, latent mean differences (derived from partially constrained SEM models) in the

different SUEIT EI subscales were also investigated.

Overall the results of the construct, method and item bias investigations suggested that the

transportability of the instrument is not severely affected when used in other Western

cultures. Almost no significant latent mean differences on the various EI facets were

evident between Western cultural groups (i.e. New Zealand and USA compared to

Australia). Evidence of cultural bias, when the instrument was applied to respondents from

non-Western cultures, was found. In addition, notable significant latent mean differences

between Australia and the non-Western cultural groups, on various EI facets, emerged. The

results suggest that it may be necessary to adapt the SUEIT for future cross-cultural use.

The practical implications of the results within the workplace, as well as limitations of the

study and recommendations for future research were discussed.

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LIST OF TABLES

Table 1 Index scores for countries on Hofstede cultural dimensions (Hofstede, 1980,

2001; Sithole, 2001) 35

Table 2 Societal cluster classification according to the GLOBE study (Gupta, Hanges,

& Dorfman, 2002) 35

Table 3 Theoretical framework of predicted cultural bias in (approximated) content of

selected SUEIT and Bar-On EQ-i: S items 64

Table 4 Cultural Distance calculated according Kogut and Singh (1988) index based

on all four Hofstede (1980, 2001) cultural dimensions 74

Table 5 Cultural Distance calculated according to Kogut and Singh (1988) index based

on Individualism and Power Distance Hofstede (1980, 2001) cultural

dimensions 74

Table 6 Cultural Distance calculated according to Kogut and Singh (1988) index based

on Individualism, Power Distance and Uncertainty Avoidance Hofstede (1980,

2001) cultural dimensions 74

Table 7 Predicted method bias for each cultural group included in this study 79

Table 8 Predicted latent mean differences for each cultural group (relative to

Australia) on three SUEIT subscales, based on Hofstede (1980, 2001) cultural

dimensions (dimension scores indicated next to each dimension) 84

Table 9 South African home languages (South African Department of Census and

Statistics, 2001) 89

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Table 10 MACS procedure to test for DIF (Chan, 2000) 91

Table 11 Four different SUEIT factor structures (measurement models) 94

Table 12 Industry representation per sample 97

Table 13 Subscale internal reliabilities, means and standard deviations for Australian

split samples for models M2 and M2a 109

Table 14 Goodness-of-fit statistics results of the single-group SUEIT CFAs for

Australia A (n=1604) and Australia B (n=1605) samples 110

Table 15 Summary of RML completely standardised parameter estimates for model

M2a for Australia A and B 111

Table 16 Subscale internal reliabilities, means and standard deviations for New Zealand

(n=234) models M2 and M2a 112

Table 17 Goodness-of-fit statistics results of the single-group SUEIT CFAs for New-

Zealand (n=234) 113


M2a for New Zealand 114

Table 19 Subscale internal reliabilities, means and standard deviations for USA (n=287)

models M2 and M2a 115

Table 20 Goodness-of-fit statistics results of the single SUEIT CFAs for USA (n=287)

115

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M2a for USA 116

Table 22 Subscale internal reliabilities, means and standard deviations for Italian

(n=320) models M2 and M2a 118

Table 23 Goodness-of-fit statistics results of the single-group SUEIT CFAs for Italian

sample (n=320) 118


M2a for Italy 119

Table 25 Subscale internal reliabilities, means and standard deviations for Sri Lanka

sample (n=587) models M2 and M2a 120

Table 26 Goodness-of-fit statistics results of the single group SUEIT CFAs for Sri

Lanka sample (n=587) 121


M2a for Sri Lanka 122

Table 28 Subscale internal reliabilities, means and standard deviations for South

African White sample (n=290) models M2 and M2a 123

Table 29 South African White (n=290) goodness-of-fit statistics results for the single-

group SUEIT CFAs 123


M2a for the SA White sample 124

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Table 31 Subscale internal reliabilities, means and standard deviations for South

African Non-White sample (n=337) for models M2 and M2a 126

Table 32 South African Non-White (n=337) goodness-of-fit statistics results of the

single group SUEIT CFAs 126


M2a for SA Non-White sample 127

Table 34 Values for absolute fit indices over all countries for the four measurement

models 129

Table 35 Results of the Australian (n=234) and New Zealand (n=234) cross-national

configural and omnibus invariance analyses 134

Table 36 Results of the Australian (n=287) and USA (n=287) cross-national configural,

omnibus and metric invariance analysis 134

Table 37 Results of the Australian (n=320) and Italian (n=320) cross-national

configural, omnibus and metric invariance analyses 135

Table 38 Results of the Australian (n=290) and South African White (n=290) cross-

national configural, omnibus and metric invariance analyses 138

Table 39 Results of the Australian (n=337) and South African Non-White (n=337)

cross-national configural, omnibus and metric invariance analyses 136

Table 40 Results of the Australian (n=587) and Sri Lanka (n=587) cross-national

configural, omnibus and metric invariance analyses 136

Table 41 Acquiescence index 140

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Table 42 Extreme Response Index 140

Table 43 CFA results for models M2a and M2b in Australian, Sri Lanka (Sinhala) and

South African samples 142


M2b for the SA White sample 143


M2b for the SA Non-White sample 144


M2b for the Australian A sample 145


M2b for the Sri Lanka (Sinhala) sample 146

Table 48 Pearson correlations between Hofstede dimensions (IND, PD) and ARS and

ERS indices over all the samples 149

Table 49 Uniform and non-uniform bias effects for SUEIT (M2a) items 1-32 for New

Zealand, USA and SA White data 157

Table 50 Uniform and non-uniform bias effects for SUEIT (M2a) items 33-64 for New

Zealand, USA and SA White data 158

Table 51 Uniform and non-uniform bias effects for SUEIT (M2a) items 1-32 for Italian,

Sri Lanka and SA Non - White data 159

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Table 52 Uniform and non-uniform bias effects for SUEIT (M2a) items 33-64 for

Italian, Sri Lanka and SA Non - White data 160

Table 53 MACS DIF results: Australia (n=234) and New Zealand (n=234) 164

Table 54 MACS DIF results: Australia (n=287) and USA (n=287) 166

Table 55 MACS DIF results: Australia (n=320) and Italy (n=320) for EE 167

Table 56 MACS DIF results: Australia (n=320) and Italy (n=320) for UEX, EMO and

EC 168

Table 57 MACS DIF results: Australia (n=290) and SA White (n=290) for EE 169

Table 58 MACS DIF results: Australia (n=290) and SA White (n=290) for UEX,

EMS 170

Table 59 MACS DIF results: Australia (n=290) and SA White (n=290) for EMO,

EC 171

Table 60 MACS DIF results: Australia (n=337) and SA Non-White (n=337) EE,

UEX 172

Table 61 MACS DIF results: Australia (n=337) and SA Non-White (n=337)

EMS, EMO 173

Table 62 MACS DIF results: Australia (n=587) and Sri Lanka (n=587) for EMS,

EC 174

Table 63 MACS DIF results: Australia (n=587) and Sri Lanka (n=587) for EE,

UEX 175

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Table 64 Standardised mean difference (d statistic) for affected SUEIT subscales over

various cultural groups 178

Table 65 EI Latent mean difference 180

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LIST OF FIGURES

Figure 1 SUEIT 7 factor model (M2) (Stough, personal communication, 2007) 95

1

CHAPTER 1:

Introduction

1.1 Overview

Societies continue to become more culturally diversified. In part this is due to the

globalisation of world trade and increase in migrant labour groups. In addition,

multinational corporations are gaining increased influence. The international workforce

continue to become more heterogeneous and the workplace more multicultural. These

changes influence the behavioral sciences which is becoming more cross-culturally

orientated (Fontaine, 2005). Researchers and practitioners of Industrial / Organisational

(I/O) psychology should be cognisant of cultural diversity and its implications in the

workplace. One such implication is that cross-cultural psychological assessment continues

to increase (Casillas & Robbins, 2005; Van de Vijver, 2002). Test transportability is a

prevalent issue in psychological measurement.

The main aim of this research is to differentiate cultural bias from true construct variance in

a self-report measure of Emotional Intelligence (EI) in the workplace (the Swinburne

University Emotional Intelligence Test, SUEIT; Palmer & Stough, 2001). Such

investigations are necessary as tests of EI are increasingly being used extensively around

the world. For example, the Twenty-item Toronto Alexithymia Scale-III (TAS-20) (Parker,

Taylor & Bagby, 2003) has been translated into 18 languages. Spanish, French and

Portuguese translations of the English Trait Meta-Mood scale (TMMS; Salovey, Mayer,

Goldman, Turvey, & Palfai, 1995) exist (Fernandez-Berrocal, Extremera, & Ramos, 2004;

Queirós, Fernández-Berrocal, Extremera, Carral, Queirós, 2005). The Bar-On Emotional

Quotient Inventory (EQ-i, Bar-On, 1997) has been translated into 22 languages and

normative data is available in more than 15 countries (Bar-On, 2000).

Oakland (2004) report that foreign developed tests are used, in most countries, more

frequently than nationally developed tests. This implies that the comparability of

psychological measurements across different cultural groups should be investigated. More

specifically, tests of bias and equivalence should routinely be conducted as bias and

2

equivalence investigations have theoretical and practical (applied) relevance. Bias refers to

a range of factors that introduce disturbances into cross-cultural assessment. The

measurement implications of bias in terms of the comparability of scores over cultures, is

termed equivalence (Van de Vijver & Leung, 1997).

Various levels of theoretical and practical implications related to bias and equivalence in

cross-cultural measurement can be distinguished. This dissertation will focus specifically

on the implications of cross-cultural psychological measurement in the workplace. Firstly,

evidence of configural invariance (Vandenberg & Lance, 2000) / structural equivalence (i.e.

obtaining equal factor structures in various cultural groups; Van de Vijver & Leung, 1997)

indicates theoretical similarity (over different groups) in the psychological constructs

underlying the measurement. An absence of structural equivalence points towards bias at

the construct level. In practice this could mean that a given psychological construct differs

across cultural groups (Fontaine, 2005; Van de Vijver & Tanzer, 2004).

Secondly, when anomalies at the item level exist, item bias is detected (Fontaine, 2005).

Differential Item Functioning (DIF) could point towards differences in the psychological

meaning of items over cultures or inapplicability of item content in a specific culture. Two

types of item bias with different practical relevance exist. Non-uniform item bias (i.e.

differences in item discrimination) has implications at the metric invariance / equivalence

level. The practical implication of evidence of this type of bias is that latent variables are

not measured on the same metric scales across different groups. Hence, workplace

decisions (e.g. personnel selection) based on relative differences between groups on the

latent trait may not be meaningful, except where group specific norms are used to avoid

adverse impact (i.e. similar selection ratios for majority and minority groups). Uniform bias

(i.e. differences in item difficulty) exists when the regression of the observed item scores on

the latent variable differs across groups in terms of the item intercept. If assumptions of

scalar equivalence remain untested, the impact is minimal on within cultural group

decisions. This is because all scores will be affected in the same direction. However,

between group differences may be erroneously interpreted in the absence of scalar

invariance evidence (Cheung & Rensvold, 2002; Steenkamp & Baumgartner, 1998;

3

Vandenberg & Lance, 2000). Group differences may be due to measurement bias and not

real underlying differences (Van de Vijver & Tanzer, 2004). In the absence of such

equivalence investigations, the truth about group differences on the latent trait and

subsequent practical implications of group membership in the workplace is simply not

known.

Thirdly, the possibility of the presence of method bias in cross-cultural assessment test

results should also routinely be inspected. For example, national differences in response

styles (i.e. Extreme response styles; that is the tendency to use the extreme ends of a rating

scale; Cheung & Rensvold, 2000; Van Herk, Poortinga, & Verhallen, 2004; or

acquiescence, the tendency to agree with questions, regardless of question content;

Johnson, Kulesa, Cho, & Shavitt, 2005) and verbal proficiency of respondents (Church,

2001, Owen, 1991; Poortinga & Van de Vijver, 1987) may influence test results. If left

unevaluated, it may be misconstrued as substantive differences in the latent construct (Van

de Vijver & Tanzer, 2004). By demonstrating that a measure is free of culturally driven

response styles or other sources of method bias, alternative explanations for observed cross-

cultural differences may be eliminated. This may strengthen inferences that are drawn from

such data in the workplace.

The construct of EI has continued to receive extensive attention in the scientific and

practitioner literature (e.g. Bar-On, 2000; Roberts, Zeidner, & Matthews, 2001; Mayer,

Salovey, & Caruso, 1997, 2004). Generally, EI is defined as the competence to identify and

express emotions, understand emotions, assimilate emotions in thought, as well as

reflectively regulate both positive and negative emotions in the self and others (Matthews,

Zeidner & Roberts, 2002, Salovey & Mayer, 1990). For the purpose of this dissertation EI

in the workplace is defined as, “the capacity to effectively perceive, express and understand

and manage emotions in a professional and effective manner at work” (Palmer & Stough,

2001, p. 1).

This investigation focuses on the generalisability and transportability of the SUEIT, a

prominent self-report monocentered (i.e. instrument from a single, Western cultural

4

background; Van de Vijver & Leung, 2001) EI measure. The instrument is being used

extensively around the world. It could be argued that the Western cultural origin of the test

(i.e. Australia) contains descriptions of EI as defined within Australian culture. It is

proposed that the increasingly multicultural global work environment mostly advocate

value systems inherent to the Western industrialised world system (e.g. high Individualism

and low Power Distance; Hofstede, 1980, 2001). However, respondents being assessed

within these environments are increasingly coming from different cultural backgrounds

with known differentiation in cultural value dimensions (e.g. Individualism, Power

Distance; Hofstede, 1980, 2001).

As discussed previously, culture may influence the transportability of instruments on

various levels. On a broad conceptual level the central research question this study aimed to

investigate can be formulated as follows: to what extent do Hofstede (1980, 2001) cultural

dimensions systematically influence the cross-cultural transportability of self-report EI

measures? Various propositions regarding how cultural context and value dimensions (e.g.

Individualism versus Collectivism, Power Distance, Uncertainty Avoidance) that

respondents ascribe to, might render the scores derived from monocentred EI measures,

inequivalent or culturally biased (Berry, Poortinga, Segall, & Dasan, 2002) are presented.

These propositions (presented in chapter three) form the basis for all the research questions

and subsequent analyses conducted in this study.

1.2 Structure of the dissertation

This dissertation can be divided into two main sections. The first section (chapters 2, and 3)

is focused on the theoretical framework of the study. It introduces the theory and

measurement of the EI construct. A review of the factorial validity of the most prominent

self-report EI inventories and the implications for cross-cultural EI assessment is included.

It is argued that cross-cultural EI research is needed to advance our theoretical and applied

knowledge of the construct. Therefore, cross-cultural psychology and measurement across

cultures is presented as a second main theme of this study. To this end the Hofstede (1980,

2001) conceptualisation of culture and cultural dimensions (e.g. Individualism, Power

Distance) is presented. This is followed by a discussion of theoretical and methodological

5

considerations that pertain to the study of EI across cultures (chapter 3). More specifically,

a review of key aspects of three decades of emotions and culture research is presented,

whilst implications for EI conceptualisation and operationalisation within the framework of

different cultures is discussed. The discussion focuses on emotional regulation, expression

and recognition as key aspects of EI. It aims to propose how different cultures may differ

on these aspects of EI. It is argued that such differences may be a result of the fact that

cultures (with different cultural value dimensions) differentially define appropriate and

adaptive emotionally intelligent behaviours. Hence, cultural dimension differences could

introduce cultural bias into Western cross-cultural EI measures on various levels, when

applied in non-Western environments. Specific items are predicted to be susceptible to

cultural bias based on the item content which, for example, taps some aspect of

Individualism or Power Distance values (cultural dimensions upon which most nationalities

differ). In addition, the presence of cultural bias (construct, method or item bias) could

influence the metric and structural equivalence of the given instruments, when measures are

transported from one culture (Western) to another (non-Western).

The second section of this dissertation presents the empirical results (chapter 4) and general

discussion (chapter 5) of this study. The results of the cultural bias investigation of the

SUEIT when applied in various cross-national samples (Australia, New Zealand, USA,

South Africa White and Non-White, Italy and Sri Lanka) are presented in chapter 4. More

specifically, measurement invariance (structural and metric equivalence), method bias and

the DIF of the SUEIT were investigated. It was argued that the amount of cultural bias

would increase as the Cultural Distance (CD, the extent to which cultures are similar or

different; Shenkar, 2001) (Kogut & Singh, 1988) between a given cultural group (e.g. Italy)

and Australia increase. Hence, the transportability of the instrument will adversely be

affected as the CD increases. That is, the more a particular culture is dissimilar to

Australian culture (origin of the SUEIT) the more pronounced the influence of culture will

be on the transportability of the instrument. In addition, latent mean differences in the

different SUEIT EI subscales were also investigated. The practical implications of the

results within the workplace were discussed.

6

Chapter 5 provides a general discussion of the results. Overall the results of the construct,

method and item bias investigations suggested that the transportability of the instrument is

not severely affected when used in other Western cultures. Almost no significant latent

mean differences on the various EI facets were evident between Western cultural groups

(i.e. New Zealand and USA compared to Australia). Evidence of cultural bias, when the

instrument was applied to respondents from non-Western cultures, was found. In addition,

notable significant latent mean differences between Australia and the non-Western cultural

groups, on various EI facets, emerged. The results suggest that it may be necessary to adapt

the SUEIT for future cross-cultural use. Limitations of the study and recommendations for

future research are discussed.

7

CHAPTER 2:

Emotional Intelligence and Cross-Cultural Psychology

2.1 Overview

This chapter presents the theoretical framework for this study. Consistent with the title, two

main themes are addressed. First, the theory and measurement of the EI construct is

introduced. A review of six prominent self-report EI inventories’ use in different cultures /

ethnic groups is outlined. Weaknesses and strengths of such applications are highlighted.

Implications for future use of EI inventories over different cultures are discussed. Next,

cross-cultural psychology, as well as bias and equivalence as cross-cultural assessment

issues, are discussed. The Hofstede (1980, 2001) conceptualisation and measurement of

dimensions of culture is described. The discussion then focuses on bias and equivalence as

central themes in cross-cultural measurement.

2.2 Emotional Intelligence

2.2.1 The state of the field

Interest in emotions has enjoyed resurgence across a wide range of sub disciplines within

psychology, neuroscience and the health sciences in the last two decades. Various

publications in the form of article special issues (e.g., Diener, 1999; Larsen, 2000) and

books (e.g. Ashkanasy, Hartel, & Zerbe, 2000; Lord, Klimoski, & Kanfer, 2002)

demonstrate the relevance of gaining an increased understanding of emotions in workplace

behavior. The utility of emotions (Fredrickson, 2003; Bagozzi, 2003) in positive

organisational psychology (Luthans, 2002; Luthans & Jensen, 2001), as well as anecdotal

and empirical evidence indicating that emotions are related to work behavior, performance

and attitudes (Arvey, Renz, & Watson, 1998; Weis & Cropanzano, 1996) underscores the

relevance of substantial advances in understanding the structure and role of emotions in the

workplace and human behavior in general. Next to motivation, cognition, and perception,

emotions are viewed as one of the basic functions of the human psyche, and with the

relatively recent advent of the construct of Emotional Intelligence (EI), emotions are

rapidly permeating the domain of work and organisational psychology.

8

Due to the original popular-press publications (Goleman, 1995; 1998), more recent

comprehensive research texts (e.g. Bar-On & Parker, 2000; Emmerling, Shanwal, &

Mandal, 2008; Matthews, Zeidner & Roberts, 2002; Stough, Saklofske, & Parker, 2009),

mainstream media exposure (e.g. “The EQ factor”, TIME magazine, 1995) and a notable

rapid increase in academic research, Emotional Intelligence (EI) has been described as,

“…a key construct in modern-day psychological research, appearing as one of the most

widely discussed aspects of intelligence in the current literature” (Matthews et al., 2002,

p.xv). Several academic journals have, in the last few years, markedly increased

publications on the topic of EI. For example, an EI keyword search revealed 78 articles

published from 1998 to 2008 in Personality and Individual Differences alone, a leading

resource for differential psychologists. A whole issue (volume 15, issue 3) of Psychological

Inquiry (that specialises in commentary), published in 2004, was devoted to EI. Similarly,

the editors of Emotion published 9 EI articles in one 2001 issue (volume 1, issue 3),

consolidating writings on some of the constructs’ most prominent researchers (e.g. Mayer,

Salovey, Caruso & Sitarenios, 2001) well known skeptics (Zeidner, Matthews, & Roberts,

2001) as well as leading scholars in emotion and intelligence. In reviewing the literature on

the construct it is evident that it has captured the interest of scholars and practitioners alike,

often spurring extreme viewpoints in the debate over EI (Brody, 2004; Mayer et al., 2004;

Matthew, Roberts, & Zeidner, 2004). Mostly, these debates continue to be focused on the

conceptualisation and operationalisation of the construct, as is evident from continued peer

reviewed publications addressing these themes (e.g. Ciarrochi, Chan, & Caputi, 2000;

Davies, Stankov, & Roberts, 1998; Petrides & Furnham, 2000; Van Rooy, Viswesvaran &

Pluta, 2005; Zeidner et al., 2001).

Since 1990 when Mayer, DiPaolo and Salovey (1990) reported on the first scale measuring

an aspect of EI in a scientific journal (Salovey & Mayer, 1990; Mayer & Salovey, 1997),

some rather unnerving unscientific claims, mostly regarding the breadth and promise of EI,

have been proposed (Goleman 1995, 1998). For example, Goleman (1998, p.31) has stated

that, “…EI accounts for over 85% of outstanding performance in top leaders” whilst

equating EI with “zeal and persistence” (Goleman, 1995, p. 285). According to Antonakis

(2004, p. 171), “…claims about the apparent necessity of EI for leadership or organisational

9

performance are unsubstantiated, exaggerated, misrepresented, or simply false”. After

reviewing systematic research on the Mayer-Salovey-Carosu Emotional Intelligence Test

(MSCEIT) (Mayer et al., 2004), one the most prominent EI measures, Brody (2004, p.237)

remains doubtful of its predictive validity and states that, “there is not a single study

reported that indicated that EI has non-trivial incremental validity for a socially important

outcome variable after controlling for intelligence and personality…too many of the studies

are not published in peer-reviewed journals”. Others have questioned the concept of EI

itself, labeling it as an “elusive concept” (Davies et al., 1998, p.989) whilst some seemed to

have progressed in their views from, “EI appears to be more myth than science” (Matthews

et al., 2002, p.547) to “…because scientific research is just beginning, EI could indeed

mature into a construct that is theoretically meaningful, empirically important, and

practically useful” (Matthews et al., 2004, p.179). What is clearly apparent, however, is the

continued request for systematic scientific research on the EI construct (e.g. Mayer, Caruso,

& Salovey, 2000; Zeidner, Matthews, & Roberts, 2004; Barret & Gross, 2001) and that

such research should be vigorously conducted. Moreover, EI is increasingly being

connected with several cutting-edge areas of psychological science, including neuroscience

(Bar-On, Tranel, Denburg, & Bechara,

2003; Freudenthaler, Fink, & Neubauer, 2005;

Gawryluk & McGlone, 2007; Kemp, Cooper, Hermens, Gordon, Bryant, & Williams,

2005). Research on the construct continues to gain momentum with evidence from various

studies displaying an association of EI with psychosomatic and physical health (e.g.

Schutte, Malouff, Thorsteinsson, Bhullar & Rooke, 2006; Saklofske, Austin, Galloway, &

Davidson, 2007), life satisfaction (Extremera & Fernández-Berrocal, 2005; Gignac, 2006),

work performance (Van Rooy & Viswesvaran, 2004), stressor appraisal and task

performance (Lyons & Schneider, 2005), team (e.g. Jordan, Ashkanasy, Hartel, & Hooper;

2005) and academic performance (e.g. Parker, Creque, Barnhart, Harris, Majeski, Wood,

Bond, & Hogan, 2004; Austin, Evans, Goldwater, & Potter; 2005). In a recent

comprehensive critical review on the claimed role of EI in the occupational environment

(Zeidner et al., 2004) the authors concluded that future research may well demonstrate that

EI facets assess important individual differences not currently included in conventional

ability and interests assessments. This renders EI “highly influential…and important” in

10

occupational settings, a construct that may even hold the promise of a predictor with

reduced adverse impact (Zeidner et al., 2004, p.394).

2.2.2 On the conceptual coherence of EI

EI broadly refers to the competence to identify and express emotions, understand emotions,

assimilate emotions in thought, as well as reflectively regulate both positive and negative

emotions in the self and others (Matthews et al., 2002; Salovey & Mayer, 1990) so as to

promote emotional and intellectual growth (Mayer & Salovey, 1997). Various authors note

that a long history of theoretical and empirical work precedes the domain of emotional and

social competence (Bar-On, 2000; Mayer & Salovey, 1997; Parker, 2000). The work of

Thorndike (1920) on what he termed as the concept of ‘social intelligence’ and Gardner’s

(1983) concepts of interpersonal (the ability to understand other individuals’ emotions and

intentions) and intrapersonal (the ability to know one’s own emotions) intelligence, is

mostly regarded as providing the basis for the conceptualisation of EI.

EI is in a stage of active development as a construct (Ashkanasy, Hårtel, & Daus, 2002).

As a young construct (Cherniss, Extein, Goleman, & Weissberg, 2006) it clearly carries a

distinctive trait of maturing paradigms (i.e. an object for further articulation and

specification under new and more stringent conditions; Kuhn, 1962): the emergence and

differentiation of specific theories. This has been evident since the first formal formulation

of EI theory by Peter Salovey and John Mayer in 1990 (Salovey & Mayer, 1990) to be

followed by the work of Goleman (1995, 1998), Bar-On (1988, 2000), and Palmer and

Stough (2001). After more than a century of research, however, Matthews et al., (2004,

p.180) recently stated that, “…literature suggests there is no clear consensual definition of

EI, and the multitude of qualities covered by the concept appears, at times, overwhelming”.

Conversely, it has been suggested that it is perhaps too early to insist on an agreed upon

definition (Gohm, 2004), differing definitions tend to be complementary rather than

contradictory (Ciarrochi et al., 2000) which could be beneficial for the field (Gohm, 2004),

and similar trends with other psychological constructs, e.g. intelligence have been observed

(Matthews et al., 2004). Most often the existence of varying conceptualisations (or theories)

of a construct, by which research in EI is distinctly characterised, might be perplexing and

11

viewed as a lack of clarity regarding the exact nature of the construct. However, the

scientific research endeavour is directed to the articulation of those phenomena and theories

that the paradigm supplies (Khun, 1962). Hence, the natural evolutionary process which

embodies the ongoing development of constructs is considered to encompass the differing

definitions thereof and changes in the way social scientists view them. This process

comprises manifestations of the self-correcting nature of science in which the researcher

becomes actively involved in order to contribute to the maturation of the paradigm that is

being studied. Such articulation is seen in the current debates in the EI domain that reflect

vigorous efforts of researchers to clarify, explain and demonstrate the full notion, purpose

and nature of EI.

Based on the different theoretical and operational developments, EI can typically be

organised into one of two complementary types: ability models or trait models (Petrides &

Furnham, 2001). Trait models (e.g. Bar-On’s model, 1997) describe EI as a constellation of

emotion-related self-perceptions and dispositions. Ability models (e.g. Salovey & Mayer,

1990; Mayer & Salovey, 1997) view EI as a set of cognitive-emotional abilities.

The two types of models (or conceptual frameworks) are best reflected by the two

paramount approaches to the assessment of EI: performance based versus self-report

measures. The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT V.2, Mayer,

Salovey, & Caruso, 2002), preceded by the MSCEIT Research Version 1.1 (Mayer, Caruso,

& Salovey, 2000) and the Multi-Factor Emotional Intelligence Scale (MEIS, Mayer,

Caruso, & Salovey, 1999) is the only set of performance based measures available. Mayer

et al. (2004) hold that a growing body of research supports its validity. However,

inconsistency in factor analytic research is apparent. For example, Gignac (2005) and

Roberts, Schulze, O’Brien, MacCann, Reid, and Maul (2006) report varying support for the

three plausible models (one, two and four factors) suggested by Mayer et al., (2003). In

addition, some question the use of consensual scoring to measure cognitive ability (Brody,

2004; Roberts et al., 2001). The reliability of the current version (MSCEIT V.2) and its

predecessor, the MEIS, has also been under scrutiny (e.g. Ciarrochi et al., 2000; Matthews

et al., 2002; Roberts et al., 2001).

12

Trait models employ self-report or observer ratings to assess EI. This dissertation aims to

investigate various aspects of the transportability and cross-cultural applicability of a self-

report measure of EI (i.e. the SUEIT; Palmer & Stough, 2001). Hence a review on the six

most popular self-report EI inventories follows. The factorial validity (i.e. evidence of

agreement and confirmatory analyses of conceptual measurement models of EI) is a key

area of evidence that is needed before claims regarding the utility of the assessment and

development of the EI construct can be substantiated (Keele & Bell, 2008). Available

empirical research regarding the factorial validity of these well-known self-report EI

inventories will be discussed. Often these measures are translated and used in other cultural

groups. Consequently, a comprehensive overview of the current extent of the practice of

cross-cultural EI research and assessment, related to a selection of well known self-report

inventories1, is included.

2.2.3 Self-report EI inventories: a review of cross-cultural applications

Twenty-item Toronto Alexithymia Scale-III (TAS-20)

A well-known conceptually related (inverse) construct to EI is Alexithymia (Parker, Taylor,

& Bagby, 2001; Schutte et al., 1998) which literally means, “without words for emotions”

(Bagby, Parker, & Taylor, 1994; Bagby, Taylor, & Parker, 1994). The Twenty-item

Toronto Alexithymia Scale-III (TAS-20) is a 20-item self report scale that measures three

dimensions, (a) difficulty identifying feelings, DIF (e.g. “When I am upset, I don’t know if

I am sad, frightened or angry”), (b) difficulty describing feelings, DDF (e.g. “I find it hard

to describe how I feel to other people”) (c) externally orientated thinking, EOT (e.g. “I

prefer talking to people about their daily activities rather than their feelings”) (Parker et al.,

2003). The three factor structure, which corresponds to the theoretical construct of

alexithymia, has been replicated in various samples (Bagby et al, 1994; Parker, Bagby,

Taylor, Endler, Schmitz, 1993; Loas, Corcos, Stephan, Pellet, Bizouard, Venisse, Perez-

Diaz, Guelfi, Flament & Jeammet, 2001; Parker et al., 2003). Others provide evidence for a

two factor structure (Erni, Lötscher, & Modestin, 1997) although the application of

1 This review included the six most prominent EI inventories in order to assess the general current stance of

cross-cultural EI research in a wider variety of self-report inventories. At the time of the review, no cross-

cultural research on the SUEIT existed.

13

principle components analysis to explore multiple solutions in these studies, have been

criticised (Parker et al., 2003).

The TAS-20 is a widely used measure. Translations into 18 different languages, with

evidence of cross-language equivalence with the original English version, as well as CFA

data from 19 different countries exist (Taylor, Bagby, & Parker, 2003). Hence, Taylor et

al., (2003) conducted a review on the reliability and factorial validity of the instrument in

the available languages and cultures. The authors propose that, “the replication of the three

factor model in other cultures and languages would provide support for regarding

alexithymia as a universal trait” (Taylor et al., 2003, p.278) as some have argued in favour

of the culture bound nature of alexithymia (Loiselle & Cossette 2001; Prince, 1987)

reflecting the Western health care professional’s emphasis on introspection and

psychological mindedness. The review provides partially incomplete fit indices (i.e. χ2/df

ratio, GFI, AGFI, RMSR, RMSEA, & TLI) for single-group CFA results obtained for 17

different language translations of the instrument. The results for the Turkish and Chinese

studies were omitted from the review. The authors state that, “no meaningful factor

structures emerged” (Taylor et al, 2003, p. 278) as well as that, “insufficient information

pertaining to adequacy of translations, samples and data collection methods hampered

inclusion of this data into the review”.

The practice of fitting single group CFA measurement models of a translated version of a

scale (e.g. French version of TAS-20) is used to evaluate whether the scale measures the

construct (alexithymia) at a structural equivalence level in relation to the country (and

culture) where the original version of the scale was developed (e.g. Canada) (Taylor et al.,

2003). According to Diamantopoulos and Siguaw (2000) this practice is a ‘validity

extension’ procedure, a special case of model cross-validation where a single model is

fitted to a validation sample drawn from a different population than the original population.

This “loose replication strategy” (Bentler, 1980) is functional in that it provides, “…a

mechanism for evaluating and replicating solutions in terms of parameters estimates and

goodness of fit…” but has been criticised as not being a true cross-validation, “…since the

analysis of the validation sample (e.g. TAS-20 French or Greek samples, Taylor et al.,

14

2003) in no way depends on results from the analysis of the calibration sample” (i.e.

original English sample, Bagby et al., 1994; Bagby et al., 1994) (MacCallum, Roznowski,

Mar, & Reith, 1994, p.13). In addition, Diamantopoulos and Siguaw (2000) argue that often

no distinction is made between “validity extension” and “validity generalisation” (the latter

refers to the process of identifying the model from a set of competing alternatives that

replicated best across different populations). They underscore the importance of this

distinction by pointing out that, “…a model can replicate well in a different population but

another model may replicate even better, in such a case, the latter model would be

considered superior than the first in terms of validity generalisation potential”

(Diamantopoulos & Siguaw, 2000, p.140). For example, in a recent CFA study of the TAS-

20 (Gignac, Palmer, & Stough, 2007) the authors report evidence for five substantive

factors – DIF, DDF, two EOT factors, a “global” alexithymia factor, as well as a negatively

keyed item method factor.

This could have two implications for further cross-cultural research which informs on the

potential transportability of the instrument to different cultures. Firstly, one could argue that

only when competing factor structures (e.g. five factor, Gignac et al, 2007; or two factor,

e.g. Erni et al, 2007) have been subjected to validity generalisation procedures (in

conjunction with the three factor model), could more confident claims of “universality” of

the three factor structure of the alexythemia construct be warranted. For example, the

absence of evidence in support of the three factor structure of alexithymia in the Turkish

and Chinese samples might be due to the fact that the construct does not translate to these

cultural contexts. This may point to the presence of construct bias2 when the TAS-20 is

transported to these cultural groups. Secondly, one could further argue for the application

of “tight” or “moderate” replication strategies by using multi-group CFA (configural,

metric, scalar invariance, Vandenberg & Lance, 2000). These procedures allow models to

be fitted to several samples simultaneously and enable the specification of invariance

constraints (Diamantopoulos & Siguaw, 2000). As Steenkamp and Baumgartner (1998)

2 Construct bias occurs when a psychological construct only applies in a specific cultural context. It can refer

to the absence of evidence for a comparable pattern of relationships with other constructs across cultural

groups. It can also refer to construct under representation by a given instrument in one cultural group

compared to another. That is, large differences in behavioural repertoire are associated with the construct.

Hence, identical indicators cannot be used meaningfully across cultural groups (Fontaine, 2008).

15

points out, cross-national differences in scale means might be due to actual true differences

between countries on the underlying construct or due to systematic biases in the way people

from different countries respond to certain items. Hence, the absence of measurement

invariance (MI) evidence impact on various levels of inferences that can be drawn from

empirical data from various cultures. For example, should the goal of the research be to

explore the basic meaning and structure of a construct cross-nationally, with the aim of

establishing whether the construct can be conceptualised in the same way across countries

(e.g. Taylor review), then the minimum requirement of configural invariance should be met

(Steenkamp & Baumgartner, 1998). In the Taylor et al., (2003, p.282) review the authors

conclude that,

“the finding of equivalent factor structures for the TAS-20 across many different cultural groups

supports that cross-cultural validity of the alexithymia construct, but should not imply that mean scores from

similar samples are comparable….although differences in mean TAS-20 scores might reflect cultural

differences in the meanings given to certain TAS-20 items, the findings also suggest that some ethnic and

cultural groups may be more alexithymic than others….the replication of the three-factor model of the TAS-

20 in highly diverse cultures supports the use of the scale in cross-cultural research. It also casts considerable

doubt on the view that alexithymia is a culture-bound construct, and suggests that alexithymia may be

universal trait that transcends cultural differences.”

Two points of concern should be addressed here. Although the authors rightly point out that

mean scores are not comparable (in an absence of MI evidence, as the case in this study),

conclusions that “some cultural groups might be more alexithymic than others” could be

erroneous as such a conclusion is based on the interpretation of latent mean scores over

groups. For example, in another study Parker, Saklofske, Shaughnessy, Huang, Wood, and

Eastabrook,(2005) report results from a TAS-20 study on two North American aboriginal

adult samples (n=123 and n=102). Single group CFA data is reported and the authors

proceed to calculate significant mean differences (ANOVA) between a large non-aboriginal

(n=1910) and aboriginal sample (n=123). No significant differences between the groups

were found. However, the absence of MI evidence may render the interpretation of this

result ambiguous. The meaning of the latent mean scores may not be comparable over these

two groups, if the lack of equivalence evidence is taken seriously.

16

Secondly, the absence of configural and metric invariance evidence most certainly cast a

doubt on the use of the scale in the context of cross-cultural research. “The cross-cultural

psychology literature suggests that the metric equivalence (invariance) and the relationships

among constructs in a model must be established before any conclusions regarding the

generalisability of a theory can be made cross-culturally” (Darvasula et al., 1993, p. 626).

Perhaps more rigorous cross-cultural research on the alexithymia construct is needed before

such strong assertions about the cross-cultural universality of the trait – and theory

underlying the construct - are justified. To this end, systematic construct, method and item

bias studies should be pursued. A better understanding of the cultural nature of alexithymia

may help elucidate the cultural specificity of the construct as well as its relationship with EI

(and the possible cultural universality / specificity thereof).

Bar-On Emotional Quotient Inventory (EQ-i)

The EQ-i (Bar-On, 1997) is described as a self-report measure of emotionally and socially

competent behavior (Bar-On, 2000). According to Bar-On, Brown, Kirkcaldy and Thomé

(2000, p. 1108), “…the Bar-On model appears to be the most comprehensive and inclusive

conceptualisation of non-cognitive intelligence” probably well evidenced by the fact that

the instrument has been translated into 22 languages with normative data available in more

than 15 countries (Bar-On, 2000) and has been noted by some as one of the most widely

used EI measures (Van Rooy & Viswesvaran, 2004). The EQ-i renders a total EQ score and

five composite scale scores (intrapersonal EQ, interpersonal EQ, stress management EQ,

adaptability EQ and general mood EQ) comprising of fifteen subscale scores. The

instrument has various different versions (e.g. Youth version, EQ-1:YV, Multirater

instrument, EQ-360).

Published versions of the EQ-i are available in Spanish, French (Canadian), Dutch, Danish,

Swedish, Norwegian, Finnish, and Hebrew (Bar-On, 1997). At the time of the publication

of the technical manual of the EQ-i, research versions were available in Arabic, Chinese,

Czech, German, Korean, and Russian. Several others were being developed (e.g. Afrikaans,

Latvian, Estonian, Iranian and Portuguese) (Bar-On, 1997). Despite being widely used in

Western and non-Western cultures, peer-reviewed research validating the factorial structure

17

(structural equivalence) of the EQ-i is lacking. Before 2003 no such studies were published

apart from research by the author himself (Palmer, Monacha, Gignac, & Stough, 2003;

Wong & Law, 2002). A Lebanese adaptation and validation study of the youth four factor

version (Bar-On EQ-i: YV; Bar-On & Parker, 2000), reported a higher order two factor- 8

subscale structure for the original version (Hassan & Sader, 2005) whilst another study

reported an adequate fit of the four factor structure in an aboriginal youth sample in Canada

(Parker et al., 2005).

No cross-cultural or ethnic group differences are reported for the EQ-i in the technical

manual, although the North American normative sample (n=3831) were comprised of 5

different cultural groups (i.e. White, 79%; Asian, 8.1%; Black, 7.1%; Hispanic/Latino, 2.8,

Native American/Aboriginal, 0.7%; Other, 2.3%). A retrospective method bias evaluation

is reported. Relatively low inter-correlations between the 15 subscales and the Positive

Impression (“faking good”) scale (an overall average of 0.19) is interpreted as indicative

that, “the general degree of social desirability response bias for the EQ-i as a whole is

relatively low” (Bar-On, 1997, p.85). Similarly, Dawda and Hart (2000), in a study on the

reliability and validity of the EQ-i, claimed that EI scores were not unduly affected by

response styles or biases. However, some evidence for response style effects is reported for

the normative sample results of the EQ-i: S (Bar-On, 2002). In an investigation of ethnic

differences, a series of 2-way ANOVAs (ethnic group by gender) revealed no main or

interaction effects3 for ethnic group and gender for any of the 5 subscales, or the total EQ

scale. The sample ethnicity included Caucasian/White (n=225, random sample from the

bigger normative sample), Black/African (n=205), Asian (n=220), and Hispanic (n=86)

respondents. However, a main effect was found for ethnic group on the Positive Impression

scale. Black respondents scored significantly higher than their Hispanic or Caucasian

counterparts (Bar-On, 2002), perhaps suggesting the presence of cultural differences in

response styles (i.e. method bias) in the data. Whilst this response style may be a culturally

driven source of method bias, when the test is transported to another culture, another source

of method bias should also be considered. For example, Grubb and McDaniel (2007)

3 It should be noted that no equivalence / invariance evidence was provided, and hence the same limitations as

mentioned earlier, in the interpretation of these findings may apply.

18

propose that EQ-i: S test scores can relatively easily be faked (i.e. in terms of social

desirability). They report that scores can be faked up to 0.83 SDs by respondents (a sample

of USA undergraduate business students) if instructed to do so. This underscores the

importance of method bias investigations (for the EQ-i: S and other EI inventories),

especially within the context of cross-cultural assessment.

Bar-On’s work has been met with some skepticism by EI scholars. For example, concerns

about the potential of the EQ-i to clearly map out individual differences in emotional and

social competencies which is purported to, “aid in the more in-depth psychodiagnostic

assessment process” (Bar-On, 1997, p.152) was raised after the results of the study by

Palmer et al., (2003) failed to replicate the dimensional factor structure of the instrument.

Other authors emphasise that Bar-On’s research have not been published in available peer-

reviewed scientific journals (Hedlund & Sternberg, 2000).

Trait Meta-Mood scale (TMMS)

The TMMS had its origin in research by Salovey, Mayer, Caruso, Goldman, Turvey, and

Palfai (1995). The instrument was developed to measure more stable individual differences

in the qualities of the reflective mood experience. It is based on the early cognitive / ability

model of EI of Mayer and colleagues and was constructed after Mayer and Salovey started

to voice their discontent with the broadening of the EI framework (inclusion of non-

intellective components). The TMMS measures individuals’ perceived ability to manage

and regulate emotions in an effective manner, as opposed to their actual EI level or capacity

(Mayer, Salovey, & Caruso, 2000) and hence is considered a proxy for perceived EI

(Salovey, Stroud, Woolery, & Epel, 2002). The 30-item inventory includes three sub-

scales. The Attention subscale (13 items) indexes perceived ability to attend to moods and

emotions, the Clarity subscale (11 items) which indexes perceived ability to understand and

discriminate between different moods and emotions, and the Mood Repair subscale (6

items) which indexes perceived ability to maintain positive and repair negative moods and

emotions. Spanish, French and Portuguese translations of the scale exist. Two peer-

reviewed articles related to these translated inventories are available (Fernandez-Berrocal et

al., 2004; Queirós et al., 2005). The original three factor structure was replicated through

19

the use of principle components analysis with varimax rotation in the modified Spanish

short version (Fernandez-Berrocal et al., 2004). Up to 2003, the dimensional structure of

the TMMS has only been investigated by the authors of the test (i.e. Salovey et al., 1995).

An Australian study confirmed the original three factor structure of the inventory, although

support for a four factor structure was also found (Palmer, Gignac, Bates, & Stough, 2003).

The means of the Clarity and Repair subscales in the Australian sample (i.e. general

population sample, mean age of 39) differed significantly from the original American

psychology student sample (reported in Salovey et al., 1995). The authors of the Australian

study conclude that, “…this finding highlights potential differences in the way sub-

populations respond to the TMMS and the need to establish sub-population and cross-

cultural norms for the scale” (Palmer et al., 2003, p. 56).

Trait Emotional Intelligence Questionnaire (TEIQue)

Another recently published EI inventory is the TEIQue (Petrides & Furnham, 2003). The

test provides an operationalisation of Petrides’s (2001) model of EI. The model was

developed in the United Kingdom by content analysing prominent models of EI and similar

constructs in the literature. The sampling domain of trait EI (also referred to as trait

emotional self-efficacy; Petrides, Pita, & Kokkinaki, 2007a; Petrides, Pérez-González,

Furnham, 2007b) comprises personality facets that are specifically related to affect. The

153 item inventory assesses a 15 facet, four factor dispositional conceptualisation of EI.

The 15 subscales include adaptability, self-motivation (both not belonging to any particular

factor), self-esteem, trait happiness, trait optimism (all well-being factor), emotion

regulation, stress management, impulsiveness (low), (all self-control factor), emotion

perception, emotion expression assertiveness, relationship skills, empathy, (all emotionality

factor), social competence, emotion management (others), assertiveness (all sociability

factor) (Mikolajczak, Luminet, Leroy, & Roy, 2007). The inventory was developed to,

“…cover the sampling domain of the construct comprehensively” (Petrides & Furnham,

2003). Conceptually, it would seem that this model maps closely on the Bar-On EQ-i (Bar-

On, 1997), as the content of at least 10 of the 15 TEIQue subscales can be related to various

Bar-On subscales. Petrides et al., (2007) argue that the Bar-On model (Bar-On, 1997) that

underpins the EQ-i is “conceptually flawed” as some salient aspects of the trait EI domain

20

are omitted and hence include an “emotion mastery” scale in their studies with the EQ-i.

The measure boasts more recent rigorous peer-reviewed empirical evidence of criterion and

incremental validity for the trait EI conceptualisation (Petrides et al. 2007a, Petrides et al.,

2007b) than the Bar-On EQ-i. A cross-cultural arm of the TEIQue research programme is

briefly mentioned by Petrides et al., (2007). Three peer-reviewed publications that report

results based on French (Mikolajczak, Luminet, Leroy, & Roy 2007), Greek (Petrides et al.,

2007b) and Spanish (Petrides et al., 2007a) translations of the inventory, exists. For the

Spanish adaptation, experts rated the 15 subscales’ content validity (relevance of subscales

to construct), as well as the “clarity and comprehensibility” of the items to ensure full

linguistic equivalence of the English and Spanish versions (Petrides et al., 2007a). In this

study, unfortunately, the factorial validity of the Spanish version is not reported, as the

analyses to determine the predictive incremental validity of trait EI over basic mood

dimensions in a clinical context, utilised only the trait EI global score (Petrides et al.,

2007a). The original British four factor structure (Petrides, 2001) was replicated in a

French-speaking Belgium sample (n=740) with a principle axis factor analysis. No CFA

results are reported, but other established recognised procedures to test the congruence of

factor structures at three levels (between Belgian and British data sets) were conducted. The

results supported the factorial invariance over the two samples and suggest “…a high level

of compatibility between the United Kingdom scoring key and Belgium factor solutions”

(Mikolajczak et al., 2007). Petrides et al., (2007b) also report evidence for the discriminant

validity of trait EI, as measured by the TEIQue, from the Giant Three and Big Five

personality dimensions in a Greek sample. In the two joint Principle Axes Factor analyses

reported, it should be noted that the oblique trait EI factor was defined by only 11 (with

Eysenck Personality Questionnaire, EPQ) and 8 (with the Traits Personality Questionnaire)

of the original 15 TEIQue scales. The results are noted as a replication of results from

British and New Zealand samples (Petrides et al., 2007b), although the latter appears to not

be published. In their study, Mikolajczak et al., (2007, p. 350) conclude that,

“…a very similar structure of affect-related personality traits can be found in French and English (as

well as in Spanish, New Zealand, and Greek; see previous text). Although there may be cultural differences

between these countries with respect to the way a trait is expressed, such cultural differences do not seem to

affect the structure of traits in the individual…it is the first EI test / questionnaire to show stability across

studies and languages”

21

Two remarks are warranted here. A consolidated publication of the factorial invariance of

the various datasets utilised in the above mentioned studies, may strengthen the

generalisability of this trait EI theory over the various language groups, substantially.

Secondly, the use of CFA invariance procedures, mentioned previously, may add very

useful additional evidence, to validate the cross-cultural validity of the theory.

Schutte Self-Report Inventory (SSRI)

The SSRI is a 33 item self-report EI inventory (Schutte, Malouff, Hall, Haggerty, Cooper,

Golden, & Dornheim, 1998). This “leading brief EI scale” (Chapman & Hayslip, 2005, p.

155) was developed by utilising the original Salovey and Mayer (1990) model and has been

used in a number of studies (e.g. Petrides & Furnham, 2000; Ciarrochi et al., 2001).

Criticism is mostly directed at the uni- versus multi-dimensionality factor structure debate,

and unbalanced use of negatively worded items in the scale (Petrides & Furnham, 2000;

Saklofske, Austin, & Minski, 2003). The latter has been addressed in a study by Austin,

Saklofske, Huang and McKenney (2004, p.556) as it could, “…potentially lead to a

confounding of EI score with acquiescent responding” which is an important issue in cross-

cultural assessment. Acquiescence, a type of differential response style, is a known source

of bias in cross-cultural assessment (Sekeran, 1983; Hofstede, 1980, 2001). The results

indicated that the modified 41-item scale (20 forward-keyed and 21 reverse-keyed items)

obtained internal reliability (overall EI) similar to that reported for the 33-item version

(with only three reverse-keyed items). The authors conclude that there are no strong

advantages of using the modified version, rather than the original general scale (Austin et

al., 2004). The findings of this study, however, add another caveat to the rather extensive

ongoing debate regarding the factor structure of the inventory. Originally Schutte et al.

(1998) proposed the SSRI to be a unidimensional EI measure. A Principle Component

Analysis on the original pool of 62 items extracted four components which they rotated

orthogonally (n=346, combined student and general population sample). Only items with

loadings greater than 0.40 on the first component were retained. This 33 item version was

published in the public domain (Schutte et al., 1998). The one-factor solution contained

scale items that represented three categories: appraisal and expression of emotion in the self

and others, regulation of emotion in the self and others and utilisation of emotions in

22

solving problems (Shutte et al., 1998). Petrides and Furnham (2000) investigated the

Schutte et al., (1998) uni-dimensionality inference (n=260, university student sample).

They could not replicate the one factor solution through CFA and presented evidence for a

four-factor structure (maximum likelihood, orthogonally rotated) which has been replicated

by Saklofske et al., (2003) and has been used in other studies (Ciarrochi et al., 2001;

Ciarrochi, Dean, & Anderson, 2002). However, the more recent Austin et al. (2004) study

report a three factor structure for both the original and modified version of the scale (PCA

with Oblimin rotation, n=500, student sample). The three factors labeled (Optimism/Mood

Regulation, Utilisation of Emotions and Appraisal of Emotions) corresponded with the

Petrides and Furnham (2000) and Saklofske et al., (2003) four factor results, although the

Social Skills factor did not emerge in this study. Recently, Gignac, Palmer, Manocha, and

Stough (2005) theoretically proposed that due to the fact that the SSRI was originally

developed based on the Mayer and Salovey (1990) model, a six factor structure (see Gignac

et al., 2005 for discussion) is a more, “plausible model of the dimensions within the

inventory in comparison to the four-factor model” (Gignac et al., 2005, p.1032). A first-

order acquiescence factor was also modeled, and five of the original items were not

included in the analysis as they could not be classified into any of the six hypothesised

dimensions. The results of the nested CFA modeling procedure did not provide support for

the six factor model (independent Emotional Expression and Emotional Regulation of

Others factor loadings were not significant). These two factors were then removed. The

modified first order, four factor and acquiescence model was practically significantly better

fitting than the Saklofske et al., (2003) four-factor model (Gignac et al., 2005).

Recently, a study of the psychometric properties of a Farsi language (Iran) translated

version of the 41-item SSRI scale (FEIS, Besharat, 2007) reported a slightly higher overall

EI reliability coefficient (α=0.89) than was obtained in the Austin et al. (2004) study

(α=0.85). In addition, a similar three factor structure to the Austin et al. (2004) results, is

reported (Principle Components Factor analysis, oblique rotation, n=442, student sample).

Negative correlations between the FEIS-41 and TAS-20 total scores (r = -0.57, p < 0.001)

as well as TAS-20 and FEIS factor scores, provided additional support for the relationship

between these constructs reported elsewhere (e.g. Parker et al., 2001, Schutte et al., 1998).

23

Moreover, significant positive and negative correlations between the FEIS total score and

Psychological Well-being (r = 0.79) and Psychological Distress (r = -0.37), respectively,

were reported. This was interpreted as support for the convergent validity of the FEIS and

EI in general.

Other translations of the original SSRI scale (Schutte et al., 1998) include a Greek modified

24 item version (Dimitriades, 2007) which measures a unidimensional EI construct (n=330,

general workplace sample). Carmeli and Josman (2006) utilised the SSRI scale to examine

the relationship between EI, task performance and organisational citizenship behaviours

(n=165, general workplace sample) in an Israeli sample. The English scale was translated

into Hebrew with a process of back translation (Carmeli, personal communication, 2008)

although this was not formally documented in the publication of the study. The total scale

reliability was acceptable (α=0.83). Zeidner, Shani-Zinovich, Matthews and Roberts (2005)

have also used a Hebrew version of the SSRI in a study of gifted versus non-gifted high

school students in Israel. They report satisfactory full score reliability (α=0.88 for gifted

and α=0.83 for non-gifted adolescents). No investigation to verify the factor structure of the

instrument in these two studies was reported. Lastly, Chan (2004) used the SSRI in a study

among Chinese school teachers. The scale was not translated into Chinese. A four-factor

structure (Exploratory Factor Analyses, Varimax rotation, n=158) is reported. The factors

include: empathetic sensitivity, positive self-regulation of emotions, positive utilisation of

emotions, and emotional awareness and appraisal. Conceptually they do not directly map

unto the Saklofske et al. (2003) results.

These studies suggest that the SSRI is being used in Western and non-Western cultural

settings. This is probably due to its availability in the public domain, as well as its

conceptual congruence with the Mayer and Salovey (1990) model. Although Petrides and

Furnham (2000) urged researchers to factor analyse the scale before using it, Chapman and

Hayslip (2005) note that some researchers have refrained from doing so, “in the interest of

retaining some coherence in the EI literature” (p. 156). For now, it would seem that the

factorial validity of this scale might stay a point of contention. The absence of consistent

24

structural equivalence evidence (in mainly Western samples) reviewed here, cast doubt on

whether this condition would be plausible in non-Western settings.

In addition, Van Rooy, Viswesvaran, and Alonso (2004) suggest that the SSRI is

susceptible to social desirability response sets (i.e. easily fakable). They found that the

measure was similar in fakability to a personality measure also used in their study. This,

together with the Gignac et al., (2005) results (evidence for first-order acquiescence factor),

may suggest that method bias could influence the transportability of the instrument, more

often than expected. Such effects may be amplified within the context of cross-cultural

assessment (as culture could drive response sets). Researchers and practitioners should be

cognisant of such effects and associated implications.

Wong and Law Emotional Intelligence Scale (WLEIS)

The WLEIS was developed in Hong Kong by Wong and Law (2002). The EI content

domain was surveyed by asking undergraduate students (n=120) to generate items for the

four Mayer and Salovey (1997) EI dimensions (self emotional appraisal, other’s emotional

appraisal, regulation of emotion, and use of emotion) that would describe a person with

high EI. The results were a 36-item preliminary measure (9 per dimension). Results from an

EFA (ML with Varimax rotation, n=189) revealed eight factors. However, “…the first four

factors with the largest eigenvalues basically represented the four hypothesised EI

dimensions” (Wong & Law, 2002, p.253) and hence the four factor structure (with only the

strongest loaded items, i.e. a 16-item version of the test) was subjected to another EFA, as

well as a cross-validation on another two student samples (sample 1, n=72; sample 2,

n=146). The indices provide relatively good evidence for the validity extension of the

measure in the two samples (sample 1: SRMR=0.08, CFI = 0.95, TL=0.93; sample 2:

SRMR=0.07, CFI=0.91, TLI=0.89). Evidence for convergent validity with the TMMS and

a brief EQ-i (20 randomly selected items) was also presented. A recent replication study by

Wang (2007) in the Beijing and Shandong provinces in mainland China on the WLEIS

(n=1458, university students) found support for the four factor structure (RMSEA=0.045,

GFI=0.97, AGFI=0.96, CFI=0.97) of the scale. The author mentions that the cross-cultural

validity of the scale should be verified with future studies (Wang, 2007).

25

Closing remarks

From this section it is clear that little congruence exist in the statistical techniques used to

investigate the factorial validity (structural equivalence) of the different self-report EI

inventories over various cultural groups / nationalities. In addition, measurement practices

are not always uniformly applied (e.g. translating scales into mother tongue before applying

in another culture). An absence of bias and equivalence evidence, when cross-group

comparisons (e.g. over different cultural groups) are conducted, exist. Method bias and its

effects on scores seem to rarely be accounted for in research studies.

A large amount of work on measurement development and the refinement of EI exist (Daus

& Ashkanasky, 2003). However, it is somewhat disconcerting that 18 years after the

construct has been coined, Keele and Bell (2008, p. 498) still point towards the fact that,

“little progress can be made with regards to predicting real life criteria unless the tools of

measurement (referring to EI measures) actually measure the scales and factors they

propose to measure”. To move forward, it is proposed that CFA procedures are used in a

validity extension and generalisation context (Diamantopoulos & Siguaw, 2000). Single

and multi-group CFA evidence for different factor structures of self-report EI measures

(e.g. three and four factor structures of the SSRI, different factor structures of the EQ-i: S

and SUEIT) should be routinely reported. This will aid in establishing which structure (and

theory of EI) has the best validity generalisation potential. It may also inform on the

universality versus cultural specificity of the construct and the transportability potential of

measures from a Western to non-Western cultural context. In addition, researchers should

be cognisant of method artifacts that may distort the meaning of quantitative differences in

scores of EI measures across different groups (e.g. cultural groups). Such effects can be

modeled within a CFA framework. Disentangling cultural bias from true construct variance

in self-report EI measures may assist in a greater understanding of the nature and utility of

the construct.

2.2.4 Moving forward with EI

Gohm (2004) in her commentary on the target article of Matthews et al., (2004) in

Psychological Inquiry (Seven myths about emotional intelligence), note cross-cultural

26

work, especially in non-Western countries, as an obvious area for further investigation to

expand current understanding of EI. Zeidner et al., (2004) admonish that it might be, “…too

early to dismiss the possibility of the existence of multiple EIs underlying emotions and

their manifestations embedded within specific cultural contexts.” Zeidner et al., (2001) note

that the work of, for example, Scherer and Wallbott (1994) show that emotions have similar

personal meanings and lead to similar response tendencies in all cultures. From this they

concluded that, “…it is unclear whether there are similar universals of EI, in that much of

what constitutes appropriate behaviour during interpersonal reaction is culturally

determined” (Zeidner et al., 2001, p. 22).

The issue of the culture fairness of expert judgments in the ability based EI measures

should perhaps be under scrutiny. For example, Mayer, Salovey, Caruso and Sitarenios

(2001) report an r=0.98 between MSCEIT consensus and expert scores. However, if the

‘experts’ in this study constituted a sample of predominant white Western men, perhaps

their views, first and foremost, is a primary reflection of cultural consensus, rather than

their specific expertise (Zeidner et al., 2004)? Zeidner et al., (2001) have proposed that the

MEIS and MSCEIT assess a kind of ‘cultural conformity’, i.e. holding beliefs about

emotion that are congruent with cultural norms. Interestingly, Roberts et al., (2001) report

no differences between ethnic groups when consensual scoring was employed (MEIS,

Mayer, Caruso & Salovey, 2000), but when expert scoring was used, White Americans

outperformed minority American groups on many of the subscales. Greenfield (1997)

argues that ability tests (like the MSCEIT for EI or Intelligence tests) presuppose a

particular cultural framework (perhaps more so than competency, trait based tests). Within

the cultural psychology perspective, that she advocates, culture implies agreement in social

convention. This requirement of universal foundational conventions must be understood

and shared by participants (when tests are exported to different cultures). For example, the

transportability of cross-cultural tests hinges on universality in values and meaning of

particular responses to particular questions. If this requirement is not met, cross-cultural

validity is compromised (Greenfield, 1997). Hence, it may be plausible to argue that

investigations with established self-report measures of EI (those included in this study and

the review) hold a greater potential than ability based measures (e.g. MSCEIT) to assist us

27

in uncovering the cultural nature of the construct (given the challenges related to cross-

cultural assessment with ability based measures in general and the MSCEIT scoring

problems mentioned above). In addition, measurement accuracy (reliability, a necessary

condition before validity is investigated) is perhaps better established for self-report

measures of EI (see review) than for ability-based measures. Hence, cross-cultural studies

with self-report EI inventories should be pursued to advance cultural specific knowledge on

the construct. This was the aim of this study. Therefore, the following section will discuss

cross-cultural psychology and assessment as the theoretical framework for this research.

2.3 Cross-cultural psychology and assessment

2.3.1 General introduction

Cross-cultural psychology is a relatively young discipline (Jahoda & Krewer, 1997). It was

institutionalised in the 1960’s, however, by the mid nineties concerns were still being raised

about mainstream psychology being ‘culture blind’ (Lonner & Adamopoulos, 1997),

ignoring culture as a source of influence on human behaviour (Segall, Lonner, & Berry,

1998). In reviewing the literature related to the history of cross-cultural psychology, it is

clear that over several decades many conceptual and semantic ambiguities related to studies

of culture and individual differences have been noted (e.g. Jahoda & Krewer, 1997; Bolski,

1996; Berry et al., 2002) perhaps resulting in misunderstandings about the discipline and

reflecting in a reluctance in the adoption thereof (Segall et al., 1998). Stemming from the

fact that the discipline of cross-cultural psychology was initially birthed within psychology

with the main emphasis on its comparative / methodological nature (see Berry, 1980) an

appraisal of the shortcomings of this approach and hence implications for the name (Segall

et al., 1998), content and purpose thereof (Boski, 1996) was to be vigorously discussed in

years to come (e.g. Lonner, 1992; Poortinga & Van de Vijver, 1994). These debates

resulted in the introduction of the term ‘cultural psychology’ as a result of the critical

assessment of the discipline being named “cross-cultural psychology” (Greenfield, 1997;

Jahoda & Krewer, 1997). Cultural psychology and cross-cultural psychology has been

described by some as two overlapping and fuzzy perspectives (Greenfield, 1997). However,

most would agree that they differ mainly on the conceptions of culture-behaviour

relationships and the methods of inquiry they utilise (Lonner & Adamopoulos, 1997;

28

Poortinga, 1997). A brief description of the distinction between the two terms is presented

next. This is needed as the theoretical framework, and associated assumptions within which

the current research is being conducted, should be clarified.

Studies that employ a cultural-psychological approach examine a particular psychological

construct (e.g. personality) in a specific cultural context (Greenfield, 1997; Poortinga,

1997; Van de Vijver & Leung, 2001), whilst emphasising the constructive characteristic of

culture (Segall et al., 1998). Reiterating this view, Berry (1994, p. 120) state that, “the

hallmark of cultural psychology is the attempt to understand individual psychological

functioning in the cultural context in which it developed.” Hence, the essence of this

approach is focused on the identification of culture specific aspects (Greenfield, 1997;

Segall et al., 1998; Van de Vijver & Leung, 2001) in psychological theories, often using

unstructured or semi-structured means of data collection (Van de Vijver & Leung, 2001) of

which the replicability of findings are generally scrutinised and questioned (Poortinga,

1997).

According to Berry et al. (2002, p.3), “cross-cultural psychology is the study: of similarities

and differences in individual psychological functioning in various cultural and

ethnocultural groups; of the relationships between psychological variables and socio-

cultural, ecological and biological variables; and of ongoing changes in these variables.” In

essence, studies in cross-cultural psychology strive to uncover universality in the cultural

diversity of human behaviour (Greenfield, 1997; Lonner & Adamopoulos, 1997) by

assuming a ‘culture-general’ approach – deliberating attempting to accommodate theories

and findings pertaining to two or more cultures (Van de Vijver & Leung, 2001). Hence, the

methodological ideal in cross-cultural psychology is to transport a procedure established in

one culture, with known psychometric properties, to one or more cultures with the goal of

making a cross-cultural comparison (Berry et al., 2002). Embedded in this culture

comparative approach (Greenfield, 1997; Poortinga, 1997) is the tendency to often treat

culture as a set of independent or contextual variables (e.g. sociocultural variables)

affecting various aspects of individual behaviour (e.g. attitudes, observed behaviours)

(Greenfield, 1997; Lonner & Adamopoulos, 1997; Poortinga, 1997). The work of the cross-

29

cultural researcher is to seek evidence of such effects (Segall et al., 1998). The

methodology of the natural sciences is mirrored in these comparative studies, with the

preference for using standard instruments and a priori formulated hypotheses which is

being tested in an experimental or quasi-experimental fashion (Poortinga, 1997). However,

failure to execute a study without paying attention to the unique methodological (e.g.

ensuring that comparisons are based on equivalent data), conceptual (e.g. the rationale for

selection of cultures for the purpose of testing the limits of some theory) and logistical

challenges can leave the results seriously, even fatally flawed (Lonner & Adamopoulos,

1997).

Nonetheless, after a seemingly slow adoption of the discipline it would seem that

researchers are increasingly realising the need for, and utility of conducting cross-cultural

research. Currently the discipline is thriving, evidenced in a consistent increase of

publications dealing with cross-cultural issues (Van de Vijver, 2002). The main goals of

cross-cultural psychology entail (a) testing the generalisability of psychological theory by

‘transporting and testing’ it in other cultures, (b) interpreting failures to generalise theory in

terms of uncovering variations in behaviour, and (c) integrating the findings of both a and b

to develop and extend a more universal psychology (Berry et al, 2002). Poortinga (1997, p.

351) provides an insightful view on universality,

“…the comparative approach is rooted in the idea of universality of psychic functioning. More precise

similarity in human psychological functioning is presumed when universality is defined at the level of

explanatory concepts as they are found in psychological theories on cognition, personality, or social

interaction. The assumption is that any theoretically meaningful personality trait, cognitive ability, emotion,

or value dimension should have universal validity. A major part of cross-cultural studies is looking for such

evidence.”

Among the listed advantages of this approach, particularly applicable to this study, is the

reduction of ethnocentrism (Berry et al., 2002; Lonner & Adamopoulos, 1997), best

described as the sense of in-group preference and favoritism, displayed in a exaggerated

tendency to think the characteristics of one’s group or race superior to those of other groups

of races (Hofstede, 2001; Munroe & Munroe, 1997). Hence cross-cultural psychology

attempts to reduce the ethnocentrism of psychology by recognising the limitations of

30

current knowledge and seeking to extend the data and theory through inclusion of other

cultures (Berry et al., 2002). However, others have argued that ethnocentrism is inherent to

the very act of engaging in cross-cultural research (Hofstede, 2001), as it is present in the

data collection instruments, choice of research topics and formulation of theories (Berry et

al., 2002) all which draw on the researcher’s notions and ideas of behaviour, influenced by

its cultural antecedents. Linked to the idea of reducing ethnocentrism, is the second benefit

of cross-cultural research: that of using the accumulated research results in culture training

programs (Lonner & Adamopoulos, 1997). The utility of EI and its applications in the

workplace would best be maximised if results of cross-cultural EI studies are utilised to

provide ‘culturally tuned’ EI development interventions in the workplace. For example,

cross-cultural research results may help human resource professionals and organisational

development consultants to better interpret and understand the cultural framework of work

behaviors and develop more appropriate interventions to enhance organisational

functioning.

This research is conducted within the cross-cultural psychology framework. It addresses

various aspects of the generalisability of the EI construct and related methodological issues

(i.e. bias and equivalence) inherent to cross-cultural EI assessment. Cross-cultural

assessment is the domain that addresses all issues related to the application of

psychological instruments (and its characteristics like reliability, validity and equivalence)

involving at least two different cultural groups (e.g. the assessment of migrant groups in a

single country or the assessment of individuals from at least two countries; Van de Vijver,

2002). Test transportability is a prevalent issue in cross cultural assessment. Culture may

influence the transportability of instruments on various levels.

2.3.2 Culture

A central question of this investigation is whether culture, as defined and measured by the

Hofstede cultural dimensions (1980, 1983, 2001), systematically influence the cross-

cultural comparability (i.e. transportability) of psychological assessment instruments?

Hence, a brief overview of Hofstede’s work follows.

31

Hofstede (2001) defines culture as,

“…the collective programming of the mind that distinguishes the members of one group of category

of people from another (p.9)….the interactive aggregate of common characteristics that influence a human

group’s response to its environment. Culture determines the uniqueness of a human group in the same way

personality determines the uniqueness of an individual…” (p.10).

His work originated in the late nineteen sixties when data was collected (1968 and 1972) in

various subsidiaries (from 72 countries) of one large multinational business organisation,

IBM, producing over 116 000 questionnaires (Hofstede, 2001). Hofstede argues that

individuals carry ‘mental programs’ that contain a component of national culture, expressed

in values predominate among people from different countries. National cultures refer to

profound beliefs, values and practices that are shared by the vast majority of people

belonging to a certain nation (Van Oudenhoven, 2001). He introduced the concept of

‘dimensions of culture’, aspects of national culture that can be measured relative to other

national cultures (Hofstede, 1991). Initially, four independent dimensions of national

culture differences, each rooted in a basic problem with which societies have to cope, but

on which their answers vary, were identified (Hofstede, 1980). The dimensions include:

Power Distance, uncertainty avoidance, Individualism – Collectivism, Masculinity –

femininity (Hofstede, 1980, 1991, 2001). A fifth dimension, long term orientation, was

later identified through research conducted by Bond (Hofstede & Bond, 1988) with the

Chinese Values Survey (CVS) among students in 23 countries. The dimensions, on the

basis of which national culture can be characterised, are briefly described next (Hofstede

1991, 2001).

Power distance relates to the basic problem of human equality. It prescribes how societies

deal with inequality between people. High Power Distance societies hold a general belief

that there should be a well defined order in which everyone has a rightful place. Low Power

Distance is associated with the prevalent belief that all people should have equal rights and

the opportunity to change their position in society. In the workplace with a large Power

Distance situation, power is centralised as much as possible and superiors and subordinates

consider each other as existentially unequal. Salary systems show wide gaps and a high

32

prevalence of supervisory personnel, structured into tall hierarchies of reporting

relationships, is evident. Small Power Distance favours decentralisation.

Uncertainty avoidance refers to the degree to which people in a country prefer structured

over unstructured situations. It ranges from relatively flexible to extremely rigid.

Uncertainty is a subjective experience, a feeling. Such feelings are expressed through

nervous stress and in a need for predictability: a need for written and unwritten rules.

According to Hofstede (1991, p. 110), “extreme uncertainty creates intolerable anxiety”.

Feelings of uncertainty are acquired and learned and ways of coping with them belong to

the cultural heritage of societies. Cultures high in uncertainty avoidance deliberately avoid

ambiguous situations by looking for structure in their organisations, institutions and

relationships which translates into clearly interpretable and predictable events. High

uncertainty avoidance favours strict rules and principles of deviant behavior.

Individualism versus Collectivism is related to the integration of individuals into primary

groups. It refers to the degree to which individual decision–making and action are accepted

and encouraged by society. In individualistic societies ties between individuals are

generally loose. Collectivist societies are characterised by people being integrated into

strong, cohesive in-groups. These in-groups protect them in exchange for unquestioning

loyalty. Collectivism favours group rewards and family enterprises, while Individualism

favours easy job hopping and individual rewards (Hofstede, 1984). The United States

scored highest in Individualism, whilst West Africa, Latino and Asian countries are at the

lower end of the scale (Hofstede, 1980).

Masculinity versus femininity. Masculinity refers to the degree to which traditional male

values (i.e. assertiveness, performance, ambition, achievement and material possessions)

prevail over female values (such as quality of life, nurturing, and warm personal

relationships). “Masculinity favours competition and survival of the fittest, while

femininity favours solidarity and sympathy of the weak” (Hofstede, 1984, p.14).

33

Data from six countries were included in this research (i.e. Australia, New Zealand, United

States of America, Italy, South Africa and Sri Lanka). Table 1 lists the original cultural

dimension scores obtained in the Hofstede study (1980, 2001) for five of the six countries.

Sri Lanka was not originally included in the Hofstede study. Recently, the results of the

Global Leadership and Organisational Behaviour Effectiveness Research Program

(GLOBE) were published (House, Hanges, Javidan, Dorfman & Gupta, 2004). This study

measured 62 societies on nine core cultural dimensions (attributes of culture). The first six

dimensions (Uncertainty Avoidance, Power Distance, Institutional Collectivism, In-Group

Collectivism, Gender Egalitarianism, and Assertiveness) had their origin in the original

four Hofstede (1980) dimensions (House et al., 2004). The 62 societies were clustered

based on religious, linguistic and economic similarities between them (Gupta & Hanges,

2004). Table 2 provides an overview of the societal cluster classification results.

Unfortunately, Sri Lanka was also not included in this study. For the purposes of this study,

it is argued that Sri Lanka falls geographically within the Southern Asian cluster (see table

2). Hence, Hofstede index scores of the other countries within the Southern Asian cluster

(Malaysia, India, Philippines, and Indonesia) are listed and were used to calculate Sri

Lanka’s stance on the cultural dimensions4.

The original Hofstede South African index scores described South African national culture

as an Anglo, individualistic and masculine culture (Hofstede, 1980). However, Hofstede’s

study (1980) was conducted during the apartheid era. The Job Reservation Act precluded

Blacks, Asians and Coloureds from employment in the IBM corporation, at the time of the

survey. In addition, the White Afrikaner population was excluded from the study to avoid

translating the questionnaire. Hence, the original Hofstede findings reflected the narrow

White South African English speaking population’s culture (Sithole, 2001). Sithole (2001)

conducted a study to investigate an all inclusive South African national culture by

surveying Whites, Coloureds, Asians and Blacks in an attempt to present South Africa’s

multicultural background and its collective influence on its national culture. Hence, the

Sithole (2001) South African national culture dimensions is presented in table 1, as well as

separate index scores for different sub-cultural groups in South Africa. It is interesting to

4 The results of the calculation are presented in tables 4 – 6 (chapter four).

34

note that South Africa obtained a higher Individualism score in the Sithole (2001) study

than in the original Hofstede (1980, 2001) study. In addition, in the Sithole (2001) study

Blacks scored almost as high on Individualism as Whites. This is opposed to the general

view that Black culture embrace collectivistic values (Sithole & Hall, 2000). However,

Collectivism has traditionally been associated with economic deprivation. Hofstede (1997)

and others (Triandis, 1994) have argued that Individualism is thought to increase as the

discretionary capital that is available to people, increases. According to Triandis (1994), as

people become more affluent, they have more freedom to “do their own thing” and

therefore, “financial independence leads to social independence” (p. 165). It should be

noted that the sample (n=572) for the Sithole study (2001) comprised of operational

employees, supervisors, middle and upper management at Portnet (a South African

parastatal transport company). The criteria for inclusion in the study were at least a high

school diploma, technical or college education. In terms of South African literacy standards

(only 22.6% of the population has a standard 10 and/or higher education qualification;

Statistics South-Africa, 2007), it was a relatively educated sample. Thomas and Bendixen

(2000) conducted a study to explore the management implications of ethnicity in South

Africa. In a middle management sample (n=586), representing Whites, Blacks, Asians and

Coloureds, they reported an Individualism score of 81. The results of this study, as well as

the Sithole (2001) study may provide preliminary evidence for the social mobility theory

(Cutright, 1968) which posits that Individualism is associated with the development of a

middle class. Hence, the Individualism score might be a result of the development of a

“back middle” class due to post apartheid transformation and democratisation in new South

African society.

35

Cultural Dimensions

Country Power

Distance

Uncertainty

Avoidance

Individualism /

Collectivism

Masculinity /

Femininity

Australia 36 51 90 61

New Zealand 22 49 79 58

South Africa

English speaking Whites**

SA National dimensions***

Black (n=388)

Coloured (n=43)

White (102)

49

49

56

59

42

49

46

57

52

22

65

77

73

68

78

63

39

48

39

21

Italy 50 75 76 70

USA 40 46 91 62

Malaysia 104 36 26 50

India 77 40 48 56

Philippines 94 44 32 64

Indonesia 78 48 14 46

Societal Cluster Classifications

Anglo Cultures

England

Australia

South Africa1

Canada

New Zealand

Ireland

USA

Latin Europe

Israel

Italy

Portugal

Spain

France

Switzerland2

Nordic Europe

Finland

Sweden

Denmark

Germanic Europe

Austria

Switzerland

The Netherlands

Germany3

Germany4

Eastern Europe

Hungary

Russia

Kazakhstan

Albania

Poland

Greece

Slovenia

Georgia

Latin America

Costa Rica

Venezuela

Ecuador

Mexico

El Salvador

Colombia

Guatemala

Bolivia

Brazil

Argentina

Sub-Sahara Africa

Namibia

Zambia

Zimbabwe

South Africa5

Nigeria

Arab Cultures

Qatar

Morocco

Turkey

Egypt

Kuwait

Southern Asia

India

Indonesia

Philippines

Malaysia

Thailand

Iran

Confucian Asian

Taiwan

Singapore

Hong Kong

South Korea

China

Japan

The next section will introduce bias and equivalence from a cross-cultural assessment

perspective. It contains a prelude to the extended propositions (presented in chapter three)

on why equivalence of scores (at item and/or subscale level) derived from a given

monocentred measure (i.e. instruments from a single, Western cultural background) over

Table 1

Index scores* for countries on cultural dimensions (Hofstede, 1983, 2001; Sithole, 2001 )

NOTE: * Power Distance and Uncertainty Avoidance are differentiated in terms of high / low scores; Individualism /

Collectivism and Masculinity / Femininity are considered to be bi-polar scales, that is, higher scores denote

Individualism / Masculinity; lower scores indicate Collectivism / Femininity; Power Distance index range from 11 –

104; Uncertainty Avoidance index range from 8 – 112; Individualism / Collectivism index range from 6 – 91;

Masculinity / Femininity index range from 5 – 95; **Original Hofstede (1980) scores derived only for English Speaking

White South Africans; *** South African National Cultural Dimension index scores derived over four most pronounced

sub-cultural groups in SA (Sithole, 2001),

Table 2

Societal Cluster classification according to the GLOBE study (Gupta et al., 2002).

NOTE: 1=White sample; 2=French Speaking; 3=Former East, 4=Former West; 5=Black sample

36

two cultures (or nationalities), may not be meaningfully comparable. The aim is to provide

a theoretical framework for predicting cultural bias in exported EI measures, by considering

a given county’s national Hofstede culture dimensions (to which the instrument is exported)

and its implications for individual items contained in self-report EI measures.

2.4 Bias and equivalence: a cross-cultural perspective

2.4.1 Introduction

Bias refers to the presence of nuisance or systematic error in measurement (Van de Vijver

& Leung, 2001). In cross-cultural assessment these ‘disturbances’ (nuisance factors)

influence the comparability of scores across cultures (Van de Vijver, 2003). That is, the

measurement implications of bias for comparability are addressed in the concept of

equivalence. It relates to the scope for comparing the scores over different cultures.

Decisions on the absence or presence of equivalence are grounded in empirical evidence

(Van de Vijver, 2003). If bias is present the differences in scores of the indicators of a

particular construct do not correspond with differences in the underlying trait or ability

(Van de Vijver & Tanzer, 1997). Two forms of bias can be distinguished: internal and

external5. The former is focused on the relationships between observed scores and latent

trait variables whilst the latter is concerned with whether culture moderates the relationship

between predictor and criterion variables (Meiring, Van de Vijver, Rothmann, & Barrick,

2005).

Byrne and Watkins (2003) suggest that problems related to cross-cultural assessment and

the nonequivalence of measures employed for such purposes, are predominantly related to

bias and translation of such measures into diverse languages, even though innumerable

other reasons may exist. For example, some argue that the cultural context defines the

meaning of behaviour and associated reactions elicited with psychometric tests that might

render the scores they provide inequivalent or culturally biased (Berry et al., 2002). Van de

Vijver and Tanzer (1997) note that bias has to do with the characteristics of an instrument

in a (specific) cross-cultural comparison, not with its intrinsic properties. Hence, the

question as to whether an instrument is biased cannot be answered in general terms. Rather

5 The internal bias of the SUEIT was investigated in this research.

37

it should be addressed as to whether an instrument is biased in a specific comparison. The

possibility of the presence of bias raise questions about the comparability of scores across

cultural groups, implying that their psychological meaning might be culture or group

dependent (Van de Vijver, 2003).

Van de Vijver and Poortinga (1997) proposed a theoretical framework for bias. Within this

framework, a lack of equivalence is commonly attributed to a host of reasons, collectively

referred to as cultural bias (Berry et al., 2002). Cultural bias in cross-cultural research is

mostly related to three sources, i.e. the construct being studied, the methodological

procedure and the item content (Byrne & Watkins, 2003; Van de Vijver & Poortinga, 1997;

Van de Vijver & Tanzer, 1997). When cultural bias is uncovered, it should be interpreted as

systematic information about cross-cultural differences which should not be equated with

measurement error (Berry et al., 2002).

2.4.2. Construct bias

Construct bias is present when the effects of a biasing factor relate to the operationalisation

of a construct, hence the construct contains a degree of disparate meaningfulness when

measured over the different cultural groups (Berry et al., 2002; Byrne & Watkins, 2003). If

construct bias exists, the psychological construct is not identical across cultures (Van de

Vijver & Leung, 1997). For example, research has shown that the dimension ‘Interpersonal

Relatedness’ in the Chinese indigenous personality measure, the Cross-Cultural Personality

Assessment Inventory (CPAI-2), does not load on any of the Big Five personality factors in

the Western model (Cheung, Cheung, Wada & Zhang, 2003). Another example is

conceptions of intelligence in non-Western cultures which includes aspects of social

intelligence not typically included in traditional Western intelligence tests (Van de Vijver &

Tanzer, 2004).

Sources of construct bias could include, firstly, the differential appropriateness of item

content. This implies that the behaviours being tapped as indicators of a construct can be

differentially appropriate across cultural groups (Berry et al., 2002; Byrne & Watkins,

2003). For example, due to cultural display rules (rules about the appropriateness of

38

emotion expressions in specific situations; Ekman & Friesen, 1975) respondents from

Asian cultures (e.g. Japanese; Matsumoto, 1989) as opposed to Australians, might view low

Emotional Control6 as ‘inappropriate’ because the culture prescribes that strong emotion

should not be freely communicated in public or the workplace. This may have an influence

on the latent mean differences between these two cultural groups on the Emotional

Recognition and Expression7 and Emotional Control subscales of the SUEIT, with an

expectation of Asians scoring consistently lower on Emotional Expression and Control.

The item “I tend to explode with anger easily” (Impulse Control subscale; EQ-i, Bar-On,

1997) might be culturally bias as cultural display rules is known to regulate the intensity of

emotional expression (an example of differential appropriateness of item content). For

example, Japan emphasises the social hierarchy much more than the USA, therefore the

social norms dictate that it is inappropriate to show strong negative emotion to a high status

person. Japanese therefore ‘qualify’ their display of negative emotions (fear, anger &

sadness) by adding a slight smile to soften the impression (Matsumoto, Yoo, Hirayama, &

Petrova, 2005).

Second and thirdly, insufficient sampling of the relevant behaviours related to the construct

or incomplete overlap of definitions of the construct across cultures, could introduce bias

on the construct level (Berry et al., 2002). Consider, for example, the inclusion of a

‘happiness’ subscale into the EQ-i (Bar-On, 1997). In European – American culture, the

right to the pursuit of happiness (e.g., made explicit in American constitution) shapes the

view that happiness should be a defining personal characteristic central to the identity of

self. Therefore, expression of unhappiness signals failure (D’Andrade, 1984) and would

possibly be equated with less emotionally intelligent behaviour in this culture. In the Asian

cultural model of emotion, moderation in emotional experience and expression serves the

fundamental belief embedded in dominant religions (e.g., Buddhism) that there is a need to

a balance between positive and negative feelings, each moderating the extent of the other

(Leu, Mesquita, & Ellsworth, 2006). Here, the inclusion of a ‘happiness’ subscale

6 Emotional Control as a dimension of EI within the SUEIT model refers to how effectively strong emotional

states experienced at work (i.e. anger, stress, anxiety or frustration) are controlled (Palmer & Stough, 2001). 7 Emotional Recognition and Expression refers to the ability to identify your own feelings and emotional

states, as well as to express such inner feelings to others (Palmer & Stough, 2001).

39

constituting of items with item content that, for example, refers to being happy with your

life, finding pleasure in life and generally being cheerful (Bar-On, 1997, 2002), may

obscure the conceptualisation of EI in Asian cultures. That is, it could be argued that

happiness may not be a central dimension that defines emotionally intelligent behaviour

within the Asian cultural context.

2.4.3 Method bias

Method bias is present if the assessment procedure introduces unwanted intergroup

differences (Van de Vijver & Leung, 1997). In this case the biasing factor (i.e. the source of

bias) influence responses on most, or all items (Berry et al., 2002). Four common sources

of method bias include differential social desirability, differential response styles (e.g.

extremity scoring and acquiescence), differential stimulus familiarity and the lack of

comparability of samples (Berry et al., 2002; Byrne & Watkins, 2003).

Social desirability bias is a general concern in self report EI measurement8. Examples have

already been discussed in section 2.2.3 of this chapter. Hofstede (1980), however, has noted

that social desirability response bias may vary by culture, resulting in a cultural specific

response set9 (Byrne & Watkins, 2003; Middleton & Jones, 2000). For example, Middleton

and Jones (2000) have provided some evidence that a significant difference in social

desirability response bias between Western (n=341) and Eastern (n=104) undergraduate

students could be attributed to differences in the dominant cultural dimensions of the

subject’s country of origin.

Jackson and Messick (1958) coined the term ‘response style’. They wanted to emphasise

the idea that useful individual difference information may be conveyed through different

styles in questionnaire responding. This type of bias, a response tendency of an individual,

will be displayed consistently across time and situations10

. Extreme Response Styles (ERS)

is the tendency to use the extreme ends of a rating scale (Cheung & Rensvold, 2000; Van

8 It should be noted, however, that in a recent study with the SUEIT, no significant or substantial relationships

between self report EI and social desirability was reported (Downey, Godfrey, Hansen, & Stough, 2006). 9 Response set refers to tendencies in responses, separate from content (Cronbach, 1950).

10 For the purposes of this dissertation, the term ‘response style’ will be used.

40

Herk et al, 2004). Acquiescence Response Style (ARS) is also known as agreement bias,

i.e. a tendency to agree with questions, regardless of question content (Johnson et al.,

2005). Sekeran (1983), for example, have suggested the influence of a ‘courtesy bias’

(especially in Asian countries) leading individuals to distort responses to please

investigators. The uncovering of strong acquiescence bias (the tendency to give a positive

answer to any question) in the work of Hofstede (1980, 2001) provides more evidence.

Randall, Huo and Pawalk (1993) have presented propositions for how key value differences

between countries (based on Hofstede’s work, 1980, 2001) might exert an individual

influence on responses to self-report questionnaires, but no empirical data was included in

the paper to investigate the propositions.

Within cross-cultural psychology, ERS and ARS are generally viewed and studied as

products of cultural differences in response styles. For example, in Western cultures high

Individualism (Hofstede, 1980) has been associated with less ARS (Van Hemert; Van de

Vijver, Poortinga & Georgas, 2002) and has been shown to not be related to ERS (Johnson

et al., 2005). These types of method bias may be more pronounced in scores obtained from

non-Western societies characterised by Collectivism and high Power Distance (e.g.

Malaysia, India). For example, high Power Distance has been associated with more ERS

(Johnson et al., 2005) and more ARS (Van Hemert et al., 2002). Collectivism has also been

found to be positively related to ARS (Smith, 2004). Demonstrating that a measure is free

of ERS and ARS eliminates alternative explanations for observed cross-cultural

differences. Such response styles may lead to invalid inferences in cross-cultural research

(Van Herk et al., 2004) if left undetected.

Stimulus familiarity (‘testwiseness’; Van de Vijver, 2003) and sample bias (Berry et al.,

2002; Byrne & Watkins, 2003) are further sources of method bias. Stimulus familiarity

might lead to different response styles, due to unfamiliarity with the use of a Likert-type

scaling format11

, resulting in response bias (e.g. only selecting extreme ends of the scale)

11

As workplace samples was used in this research, it may be argued that the respondents would all have been

previously subjected to some form of psychometric assessment (and the Likert-type response scale) as part of

the recruitment process. Hence, it may be plausible to argue that stimulus familiarity may not have influenced

the results.

41

(Byrne & Watkins, 2003). Sample bias has the potential to introduce larger cultural

distance, leading to more alternative explanations for cross-cultural differences (Van de

Vijver, 2003). Hence the comparability of samples12

should be carefully considered on

factors other than the construct being studied (Byrne & Watkins, 2003).

Verbal ability or language proficiency (i.e. bilingualism) is a sample characteristic that has

frequently been mentioned as a potential source of bias in ability testing (Claassen,

Krynauw, Holtzhausen, & Wa Ga Mathe, 2001; Foxcroft, 1997; Owen, 1991). Similar

arguments may be applicable to the domain of personality measurement, as well as other

individual differences (e.g. EI). For example, Marsh (1986, 1996) has demonstrated a

negative relationship between the observation of a negatively keyed item factor and verbal

ability, suggesting that individuals with less verbal skill may have difficulty reading

negatively keyed items, accurately, particularly those items with double negatives. Loiselle

and Cossette (2001), in a cross-cultural validation study of the TAS-20, report that Peruvian

respondents had difficulties responding to negatively worded items. Others have proposed

that individuals who do not read test items accurately or who fail to understand the content

of a test item are more likely to respond incorrectly (Hinkle, 1994; Schuttelworth-Edwards,

Kemp, Rust, Muirhead, Hartman, & Radloff, 2004). Poortinga and Van der Vijver (1987)

suggested that when investigating cross-cultural differences it is of major importance to

measure, and consider the consequences of other contextual variables, such as mother

tongue, that may influence test scores. For example, Owen (1991) report language to be a

potential source of bias in the Junior Aptitude Test when administered to different

population groups in South Africa. In another South African qualitative study, Abrahams

and Mauer (1999) examined the impact of home language on the responses to the items of a

personality questionnaire, the 16PF. They concluded that the understanding of items and

concepts in English was problematic, especially for Black groups. Less language

proficiency in the test language (e.g. testing a bilingual person in a language other than

their mother tongue) may manifest itself in item keyed method effects. Gignac (2005) have

cautioned researchers to always consider the possibility of item keyed method effects when

12

All the samples in this study were matched on age and gender in an attempt to control for at least these two

sample characteristics, and their bias effects on the results.

42

conducting CFA analysis. Poor model fit may be a result of not modeling these method

factors, together with substantive factors.

2.4.4 Item bias and Differential Item Functioning (DIF)

In relation to item content, the effects of a biasing factor can manifest in a single or few

items, known as ‘item bias’ or ‘differential item functioning’ (DIF) (Berry et al., 2002).

Poor translation or inappropriate items for a specific context may cause item bias (Van de

Vijver & Leung, 1997). Item bias involves a lack of equivalence in a separate indicator or

item (Fontaine, 2008). Hence, if individuals from different cultural groups with an equal

ability / trait / attitude do not have the same probability of giving a correct answer, item

bias exists (Van de Vijver & Leung, 1997). If removing biased items eliminates group

differences on the scale, the groups may have differed because of DIF rather than from

inherent group differences in the construct.

Whether the researcher finds a lack of measurement equivalence on a substantial number of

items on a measure (i.e. pointing to a violation of the assumption that the same construct is

being assessed across the groups by the same measure), or if evidence of partial

equivalence is found to exist (i.e. only a few items are not found to be equivalent across

groups), three prominent matters related to item bias need to be addressed before true

meaningful between-group comparisons are admissible (Chan, 2000). Firstly, the

methodological issue of detecting the items that are functioning differentially across groups

should be considered (Chan, 2000), the type of bias investigation that has historically

received the most research attention (Van de Vijver & Leung, 1997). Item bias analyses

address the second and third issues, namely, understanding the reasons for the difference in

item functioning (e.g. relating items to variables that are irrelevant to the test construct) as

well as the practical implication of how to treat differentially functioning items (e.g.

remove the items from the scale) (Chan, 2000).

With item bias, recent improvements in the development of Item Response Theory (IRT)

models accounting for responses on polytomous ordered response formats (such as Likert-

type ratings scales), should be noted. The IRT model assumes that a similar response from

43

respondents, with a similar standing on the latent trait regardless of their cultural

background, is evoked by an unbiased item (Van de Vijver & Leung, 1997). Traditionally,

item responses are linked to latent traits by utilising a logistic curve, specified by three

parameters, i.e. difficulty, discrimination and guessing parameters, although the latter is

mostly excluded when the procedure is employed for attitudinal data (Van de Vijver &

Leung, 1997) as it evident in the work of Chan (2000).

The increased development and use of polytomous IRT models to detect DIF in terms of

item difficulty and discrimination, specifically in self-report measures that measure non-

cognitive ability latent trait variables, however, are not without limitations. For example,

large sample sizes (Van de Vijver & Leung, 1997; Van de Vijver, 2003) are required and

hence the consequent effects on chi-square test results (Reise et al., 1993) are unknown.

The absence of practical fit indices to reduce dependence on sample size, as well as the

absence of modification indices (Chan, 2000) are further limitations worth mentioning. A

recent significant contribution in this area by Chan (2000) demonstrated the use of the

Mean and Covariance Structures Analysis (MACS) model (Sörbom, 1974) to detect

uniform and non-uniform DIF (an improvement upon the application of the standard

confirmatory factor analysis model, with which uniform DIF could not be examined, Reise

et al., 1993), where the item intercept and factor loading corresponds to the item difficulty

and discrimination parameters, respectively. Uniform DIF is present when no interaction

between ability level and group membership is evidenced, hence only the item level

difficulty parameter differs across groups. Non-uniform DIF is evidenced when item

discrimination parameters differs across groups, implying that there is an interaction

between ability level and group membership (Chan, 2000).

By starting off with a priori notions (although limited) regarding whether uniform and non-

uniform DIF on the three subscales of the Kirton Adaption-Innovation Inventory (KAI,

Kirton, 1976) over gender groups and occupational groups would be found, Chan (2000)

illustrates the application of a series of MACS models, run in LISREL 8 (Jöreskog &

Sörbom, 1993), providing a systematic demonstration of the modeling procedure

implemented to do so. MACS analyses (Jöreskog & Sörbom, 1989), an extension of

44

standard structural equation modeling techniques, allows for the analyses of mean-level

information in addition to the typical variance-covariance information in SEM analyses

(Sörbom, 1982). Little (1997, p.54) provides a succinct, yet insightful overview of why

MACS analyses are ideally suited to establish construct comparability (a fundamental

concern in cross-cultural research) as well as uncover between-group differences. The

reasons include that such analyses allow,

“ for (a) simultaneous model fitting of an hypothesised factorial structure in two or more groups (i.e.,

the expected pattern of indicator-to-construct relations for both the intercepts and factor loadings), (b) tests of

the cross-groups equivalence of all reliable measurement parameters (i.e. again both intercepts and loadings),

(c) corrections for measurement error whereby estimates of the latent constructs’ means and covariance are

disattenuated (i.e. estimated as true and reliable values), and (d) strong tests of substantive hypotheses about

possible sociocultural influences on the constructs (e.g. nested-model comparisons in multiple-group

analyses).”

The results firstly confirmed the expectation that no DIF across gender groups would be

found (i.e. full measurement equivalence at item level). Empirical evidence for both non-

uniform and uniform DIF across occupational groups on the KAI subscales, consistent to

expectations, were found. However, in each KAI subscale, only a small number of items

(e.g. 1 of 7 in the Efficiency subscales) exhibited DIF, leaving the author to conclude that

there is partial measurement equivalence for each KAI subscale, which implies that direct

mean comparisons across the occupational groups might probably be meaningful (Chan,

2000).

The MACS DIF approach (Chan, 2000) was utilised in this study. In addition to detecting

item bias, this approach allowed for the comparison of latent mean differences on the

specific EI subscales when the DIF items were flagged and freed in the final partially

constrained model.

A theoretical analysis of the nature of items contained in two prominent EI measures (one

of which were empirically studied in this research) is presented in chapter three. For

example, incidental differences in appropriateness of the item content (due to differences in

national cultural value dimensions respondents subscribe to), emotion words that have

45

different meanings in different cultures, as well as differences in the prototypicality of

emotion words over different cultures, may produce item bias when EI measures are

transported from a Western to non-Western cultural context.

2.5 Conclusion

This chapter presented the theoretical framework for this study. It discussed the current

status of EI research, establishing that research efforts seem to be on the increase in spite of

criticism on the construct. The present extent of cross-cultural applications of six prominent

self-report EI inventories was outlined. The review aimed to clarify current challenges in

the cross-cultural application of self-report EI measures. It also argued that more stringent

factorial and measurement invariance studies are needed before confident claims in the

universality / cultural specificity of the EI construct (measured by different inventories),

and its associated utility in the workplace, can be made. Next, cross-cultural psychology

and measurement over cultures was discussed. The Hofstede (1980, 2001)

conceptualisation and measurement of dimensions of culture was described. Bias and

equivalence are central themes in cross-cultural measurement. The last section of this

chapter presented a theoretical framework for predicting cultural bias in exported EI

measures (from Western to non-Western environments) given a country’s national Hofstede

culture dimensions (to which the instrument is exported). It was argued that construct,

method and item bias investigations should be conducted on established self-report EI

inventories. Studies that aim to disentangle cultural bias from true construct variance in

self-report EI measures (of which this research on the SUIET is a starting point) may assist

in a greater understanding of the nature and utility of the construct.

46

CHAPTER 3

Emotional Intelligence across cultures: theoretical and methodological considerations

3.1 Overview

The focus of this chapter is a brief review on key aspects of three decades of research on

emotion (i.e. emotional regulation, expression and recognition) and culture. Implications

for EI conceptualisation and operationalisation within the framework of different cultures

are discussed. The discussion centers on proposed arguments regarding possible elements

of cultural bias contained in two Western self-report EI instruments (i.e. the EQ-i:S, Bar-

On, 2002; SUEIT; Palmer & Stough, 2001) should they be used in a non-Western context.

The SUEIT model (Palmer & Stough, 2001) broadly subscribes to the Mayer and Salovey

(1990, 1997) EI model. It defines EI in terms of five dimensions (i.e. Emotional

Recognition and Expression, Understanding Others Emotions, Emotions Direct Cognition,

Emotional Management and Emotional Control1). The broader Bar-On model (1997, 2002)

proposes that EI encapsulates emotional, social and personal competencies, skills and non-

cognitive capabilities that may arise from the effective use or regulation of emotions. This

model places emphasis on adaptation to environmental demands.

Although both the SUEIT and Bar-On models are self-report approaches to measuring EI,

they differ in terms of the scope of EI sub-dimensions they include (e.g. the Bar-On model

includes a wider variety of social intelligence elements). Hence, this discussion focuses on

both these instruments to allow for a wider and richer conceptual evaluation of EI item

content (and sub-dimensions)2. In addition, both these instruments are being used

extensively around the world. For example, it has been noted previously that the Bar-On

EQ-i (Bar-On, 1997) has been translated widely and normative data exist in more than 15

countries (Bar-On, 2000). On a practical level the discussion may be useful to I/O

Psychology researchers and practitioners in terms of creating an awareness of possible

elements of cultural bias in Western EI measures, when transporting such measures to non-

Western environments.

1 Refer to section 4.4.1 for a description of each SUEIT subscale (dimension).

2 The empirical cultural bias investigation presented in chapter 4, however, only focuses on the SUEIT.

47

It is argued that the Western cultural origin of both these tests contains descriptions of EI as

defined within those cultures (i.e. Australia for the SUEIT and Canada for the Bar-On EQ-i:

S). It is proposed that the increasingly multicultural global work environment mostly

advocate value systems inherent to the Western industrialised world system (high

Individualism and low Power Distance; Hofstede, 1980, 2001). However, respondents

being assessed within these environments are increasingly from different cultural

backgrounds with known differentiation in cultural value dimensions. Hence, cultural group

membership (and associated differences in cultural value systems of respondents) could

introduce bias into Western cross-cultural EI measures when these are applied cross-

culturally (e.g. non-Western contexts). Culture influences the transportability of

instruments on various levels (e.g. structural or metric equivalence). This has implications

for research and practical workplace decisions, based on such transported instrument’s

scores. Specific items in these inventories are predicted to be susceptible to cultural bias

based on the item content which, for example, taps some aspect of Individualism or Power

Distance (cultural dimension on which nations tend to differ). For the purpose of this

discussion, national culture is defined as the pattern of values, attitudes, and beliefs that

affect the behaviour of people from different countries (Hofstede, 2001) described in terms

of the Hofstede (2001) cultural dimensions3 (discussed in chapter 2). Methodological issues

related to cross-cultural EI research are also highlighted.

3.2 A brief review of the current state of cross-cultural EI research

One area of EI research that remains a relatively unchartered domain is that of cross-

cultural EI research. Cross-cultural research aims to develop and extend a more universal

psychology by investigating the generalisability of psychological theory in different

cultures (the practice of ‘transporting and testing’). Failures to establish generalisability

(when research methodology and measurement instruments are sound) may be interpreted

in terms of cultural variations in behavior (Berry et al., 2002). This has two implications for

future cross-cultural EI research. Firstly, when monocentered instruments are used in

3 Only three of the five cultural value dimensions are deemed relevant to this discussion. They include,

Individualism – Collectivism (i.e. the relationship of the individual to the group), Power Distance (i.e. status

differentials that exist within groups) and Uncertainty Avoidance (i.e. rituals concerning the future and

avoidance of anxiety) (Hofstede, 1980, 2001).

48

generalisability studies (e.g. from Western to non-Western cultures), they are more likely to

run into problems of bias (Van de Vijver & Leung, 2001). Therefore, testing of the

equivalence of scores across different groups should routinely be conducted (Van de Vijver

& Leung, 2001). This is a weakness of the limited cross-cultural EI studies conducted up to

this point. Secondly, when cultural bias (construct, item or method bias) is uncovered, ways

to minimise bias (i.e. method bias) in EI assessment should be considered, whilst evidence

of construct and item bias should be scrutinised to better uncover the cultural variability of

the construct. This knowledge could then be applied in reducing ethnocentrism (Berry et

al., 2002; Hofstede, 2001) in current EI instruments, as well as designing better ‘culturally

tuned’ EI development programmes (e.g. Herkenhoff, 2004).

Based on the Van de Vijver and Leung (1997, 2001) taxonomy4 of studies in cross-cultural

psychology, EI research in this domain has mostly yielded psychological differences and

generalisability studies, and the empirical evidence on ethnic differences have been noted

to be both, “…scant and contradictory” (Matthews et al., 2002, p. 71). This remains to be

true for research on both the prominent ability (the MEIS and MSCEIT; Mayer et al., 2000)

and the trait model (self-report) measures of EI (e.g. EQ-i, Bar-On, 1997; SSRI / EIS,

Schutte et al., 1998; SUEIT, Palmer & Stough, 2001).

For example, a recent psychological differences study with the trait based Emotional

Intelligence Scale (EIS, Schutte et al., 1998) surprisingly reported higher total EI scores for

minority ethnic groups (Blacks, Hispanics) leading the researchers to pose the question of

whether in fact, “…majority groups could sue using a claim of test bias” (Van Rooy,

Alonso & Viswesvaran, 2005, p. 694), as group difference in mean predictor scores could

4 The taxonomy entails a 2 × 2 classification of studies in (cross-)cultural psychology, based on two

dimensions (i.e. whether the purpose of the study is hypothesis-testing or exploratory, and whether or not

contextual variables were included). Four categories are distinguished. Hypothesis testing studies include

generalisability studies which explore whether research findings obtained in one group (e.g. Western group)

can be replicated in another group (e.g. non-Western group). No contextual elements are taken into account.

Equivalence is usually assessed. When contextual factors are accounted for in hypothesis testing studies, a

contextual theory / theory driven study is conducted. Studies that have an exploratory orientation are grouped

into psychological differences (no consideration of contextual factors) or ecological linkage / external

validation studies. The former applies an instrument in two cultural groups, without any particular theory

regarding the nature of cross-cultural differences to be expected. The latter, by including a set of contextual

variables in an exploratory manner, aims to provide evidence for specific interpretation of observed cross-

cultural differences (Van de Vijver & Leung, 1997, 2001).

49

be a likely cause of adverse impact. Rozelle, Pettijohn and Parker (2002) reported

significant differences between domestic (n=219) and international students (n=76) at an

American university, in terms of overall EQ scores and individual factors on the Emotional

Quotient test (Goleman, 1995). Acknowledging that the study assumes, and not explicitly

tests, for whether cultural test score bias could be the cause of the reported cultural

differences, they conclude that opportunities for success in business for the international

students might be limited by their EI.

Three published generalisability studies, to date, on the EI construct (self-report measures)

across diverse ethnic / cross national cultural groups exist. A study of the EQ-i: YV (Bar-

On & Parker, 2000) on Canadian Aboriginal versus non-Aboriginal youth was conducted

by Parker et al., (2005). This study is exemplary in acknowledging and theoretically

proposing how cultural factors might influence the operationalisation of the construct in the

two different cultural groups. The results of the Parker et al. (2005) study provided

preliminary support for equivalence of the EQ-i: YV (Bar-On & Parker, 2000) scores over

the two groups (results of a multi-group CFA, i.e. configural invariance5, is reported),

although not to the extent that the full measurement invariance6 (i.e. configural, metric and

scalar invariance) of the instrument is explicitly investigated. In addition, consistent group

differences over the groups on the total EI score and Interpersonal, Adaptability and Stress

Management subscales (Aboriginal students scored consistently lower) as well as post hoc

discussions on possible effects and causes of these differences were presented. Evidence,

albeit limited, to support the invariant operation of the EQ-i: YV, was presented in this

study (Parker et al., 2005).

Rahim, Psenicka, Polychroniou, Zhao, Yu, Chan, Dudana, Alves, Lee, Rahman, Ferdausy,

and Van Wyk (2002) investigated the relationship of self-awareness, self regulation,

5 Configural invariance (Vandenberg & Lance, 2000) is also known as the test of ‘factor structure

equivalence’ (Hair, Black, Babin, Anderson, & Tatham, 2006). Evidence for configural invariance points

towards a similar conceptualisation of a constructs in different groups (absence of construct bias), to the

extent of the data supporting the same number of factors and similar items associated with each factor

(Meredith, 1993). 6 A lack of measurement invariance evidence is known to compromise the unambiguous interpretation of

between group differences (Byrne & Watkins, 2003; Cheung & Rensvold, 2002; Vandenberg & Lance, 2000)

rendering cross-cultural comparisons on cultural mean differences to be misleading and ultimately, possibly

meaningless.

50

motivation, empathy and social skills (Emotional Quotient Index, EQI; Rahim et al., 2002)

of supervisors to subordinates’ strategies of managing conflict. Single group CFA results

for the EQI for the data from seven countries (USA, n=303; Bangladesh, n=152; Hong

Kong and Macao, n=79; Greece, n=132; Portugal, n=86; China, n=210; South Africa,

n=84) were reported, as well as a fully unconstrained multi-group CFA analysis (configural

invariance) for each of the countries with the USA as a reference sample. No theoretical

explanations for why culture might produce differences in the cross-national CFA results

were provided. No further tests of invariance were reported. The authors suggest that the

results supported a somewhat consistent cross-country pattern, although admitting that

there were differences in results, and that, “…it is not possible to determine whether these

differences came from the small and convenience samples or differences in cultures”

(Rahim et al., 2002, p.321). It should be noted that five of the seven county samples sizes

fell below the n=200 structural equation modeling sample size guideline (Hair et al., 2006),

with the smallest sample being 79, casting doubt on the generalisability of the results.

The only cross-cultural EI study that has explicitly tested for full instrument invariance on

two early measures of self-report EI (TMMS, Salovey et al., 1995; TAS-20; Bagby et al.,

1994) was conducted by Ghorbani, Davison, Bing, Watson, and Mack (2002). By

combining the factors of these measures into an input (attention to emotions), process

(clarity of emotions) and output (repair of emotions) information-processing system, they

conducted CFA and measurement invariance procedures to fit the data, obtained from

Iranian (n=231) and American (n=220) university students, to the model. Even though CFA

and measurement invariance procedures provided evidence for cross-cultural similarities in

the fit of the a priori higher-order factor structure, subsequent analyses revealed cross-

cultural dissimilarities in the actual processing of emotional information (interrelationships

among factors differed). This confirmed the notion that contrasts between Iranian and

American social life (individualistic versus collectivistic values; Hofstede, 2001) might

have implications for the processing of emotional information in these groups (Ghorbani et

al., 2002).

51

Preliminary research on EI ability measures have proven no better in unraveling cross-

cultural differences in EI. The criterion for correctness (‘right’ answers) on ability EI test

items (MEIS, Mayer et al., 2000; Mayer, Salovey, Caruso Emotional Intelligence Test,

MSCEIT; Mayer et al., 2001) are typically based on target, expert or consensus criteria.

Mayer et al., (2000) argue that the basis for claiming ‘right’ answers is grounded in

evolutionary and cultural foundations for the consistency of emotionally signaled

information. They cite the work of Darwin on the evolution of emotion (1872 / 1965) and

that of Ekman (1972) who have provided evidence for a strong universal emotional

‘language’ and facial expression of emotion among humans. In addition, they argue that

replications across literary sources and more recently, the Internet, of ideas or ‘cultural

memes’ are comparable to biological genes. Therefore, emotional ideas are disseminated

and reproduced as popular ideas according to the degree to which they are found useful and

functional within a given culture. They conclude that consensus criterion is the best single

means of determining a correct answer by stating that, “…if one subscribes to the idea that

emotional signals evolve, either biologically or culturally, then a wide, representative,

sample of observers is probably a good judge of correctness under at least some

circumstances” (Mayer et al., 2000, p.327). Based on this reasoning it could, therefore, be

argued that when consensual scoring is used in ability measures, the possible effects of

cultural bias in this type of EI measurement might be controlled. Could this be an

alternative explanation for results reported by Roberts et al., (2001), who report no

differences between ethnic groups when consensual scoring was employed (MEIS, Mayer

et al., 2000), but when expert scoring was used, White Americans outperformed minority

American groups on many of the subscales? It should, therefore, be asked whether ethnic

differences, when uncovered with expert scoring, could be interpreted as ‘real’ differences

between these groups, as it seems plausible to argue that cultural bias effects might be

masked by expert scoring. Therefore, should consensus scoring7 not always be used to

minimise any possibility of cultural bias in ability based EI measures?

7 According to Matthews et al., (2002), the test developers of the MEIS / MSCEIT are moving towards an

operational definition of ability based consensus scoring, inferring that a person is more intelligent if they are

closer to the population norm. They question the rationale for scoring an ability on this basis, arguing that, in

this context, it is misleading to describe EI as an ‘intelligence’.

52

The goal of the preceding section was to provide a brief and by no means exhaustive

overview of previous attempts to study the EI construct over different cultures. As is

evident, many theoretical and methodological challenges implicitly embedded in any

attempt to study EI and culture, face the researcher attempting to tread this unchartered

domain.

3.3 Culture and emotion research: implications for EI

An important question central to this discussion is whether the notion of an ‘ideal’ EI

profile is context dependent, in the sense that ‘appropriate’ or ‘effective’ emotional

behaviour, will in itself be dictated by the cultural origin of the measurement instrument

used? For example, the two EI instruments included in this discussion (i.e. SUEIT, EQ-i: S)

are classified as monocentered instruments (Van de Vijver & Leung, 2001). To what extent

do such instruments and the construct they purport to measure, truly reflect the construct

and all its facets in other cultures? Moreover, when imported measures are used, invariant

psychometric properties and higher levels of equivalence of the instruments should be

investigated. Where is the research evidence to support this? According to Hui and Triandis

(1985) cross-cultural equivalence can be conceived in terms of a universality-cultural

difference continuum and different levels of abstraction and concreteness. They argue that

when imported measures are used, researchers should enhance validity and establish

different levels of equivalence in order to surmount the goal of maximising both precision

and meaningfulness of comparison in cross-cultural research. This universality-cultural

difference continuum (to what extent constructs are considered universally applicable or

meaningful in specific cultural context), also known as the etic – emic (Berry, 1969)

debate, has permeated emotions research for three decades. Research on depression, anxiety

and personality have also not proved to be conclusive on whether imported instruments

capture human psychological phenomena that are invariant across cultures (Sue & Chang,

2003). For example, Leong, Okazaki and Tak (2003) reviewed the assessment of

depression and anxiety in Asia, and concluded that some imported measures (e.g. State-

Trait Anxiety Inventory, Chinese Beck Depression Inventory) miss capturing culture

specific elements (e.g. particular symptomatology in Chinese populations) of these

constructs. Cheung et al., (2003) identified the factor, Interpersonal Relatedness, in the

53

indigenous personality measure the Cross-Cultural Personality Assessment Inventory

(CPAI-2), developed for the Chinese population. This factor did not load on any of the Big

Five personality factors in Western models, whilst they also demonstrated that it was found

among Caucasian US students who completed the CPAI-2, suggesting that Western

measures may not have captured all meaningful important personality dimension (Sue &

Chang, 2003). Leung and Wong (2003), on the other hand, assert that broad personality

patterns are universal. The successful international use and adaptation of the Minnesota

Multiphasic Personality Inventory (MMPI-2) underscores this viewpoint (Butcher, Cheung,

& Lim, 2003).

For almost three decades, emotion research has been dominated by the disciplinary

preferences of researchers, leading to an oversimplification in the debate regarding the

cultural universality or relativism of emotional experience. More specifically, psychologists

and biologists have been inclined to overlook cultural differences, whilst anthropologists

emphasise them, overlooking similarities (Ellsworth, 1994). More recent theoretical

models, have attempted to account for both universality and cultural variation by focusing

on particular components of emotion and their similarities and differences across cultural

boundaries (Fiske, Kitayama, Markus, & Nisbett, 1998; Mesquita & Frijda, 1992; Scherer

& Wallbott, 1994). Matsumoto (1989), for example, has argued that even though emotions

are biologically programmed, learning control of expression and perception is highly

dependent on cultural factors. Kitayama and Markus (1994) published a volume of research

consolidating empirical research dedicated to the premise that emotions are socially and

culturally shaped and maintained. This happens, for example, through collective knowledge

that is represented in linguistic convention (e.g., the nature of the affective lexicon and

specific meanings of emotions terms; Wierzbicka, 1994, 1999). Therefore, it could be

argued that the traits or competencies measured by self-report EI measures (per EI

dimensions, e.g., emotional control, management) tap into this collective knowledge of the

culture within which the test was developed. In administering a self-report EI instrument,

the presence (or absence) of certain ‘traits’, competencies or behavioural tendencies that

would allow a person to respond in an emotionally intelligent way to the environment, and

cope with environmental pressures, whether that be in the workplace (performance, team

54

work, leadership, ability to cope with stress, burnout; e.g. Ogińska-Bulik, 2005; Slaski &

Cartwright, 2002; Van Rooy, & Viswesvaran, 2004) or life in general (life satisfaction,

psychological and physical health; e.g. Schutte et al., 2007) is measured within the

boundaries of the cultural origin of the test. If the potential to display appropriate

emotionally intelligent behaviours is context dependent, then it might be reasoned that the

context (socio-cultural context) should be considered when the behavioural manifestations

(through which EI is often measured) of EI is captured in the development of a self-report

instrument. For example, key cultural dimensions (Hofstede, 2001) such as Individualism

versus Collectivism, high or low Power Distance and Uncertainty Avoidance could be

significant influences in this process.

The following section provides a theoretical / conceptual discussion on how cultural group

membership might introduce cultural specificity into the development of self-report EI test

items. The discussion is guided by key findings of three decades of emotion and culture

research, specifically focused on emotional appraisal and regulation. It is proposed that

cultural difference in values could introduce bias into Western cross-cultural EI measures

where these measures are applied cross-culturally. Specific items are predicted to be

susceptible to cultural bias based on the item content. Table 3 provides an overview of the

proposed affected content of EQ-i: S and SUEIT items included in this discussion (the

content in the table is approximations of selected items from these two inventories, i.e. the

item content has been slightly modified from the original items).

3.3.1 Emotional regulation in cultures

Emotional regulation refers to the processes related to influencing emotions that are

experienced, situations under which a given emotion is experienced, and how and whether

an individual expresses a given emotion (Gross, 1999). It could be argued that the cultural

dimensions of Power Distance, Individualism / Collectivism and Uncertainty Avoidance

(Hofstede, 2001) may account for cultural specificity in emotional regulation abilities in

respondents from different cultures, attenuating beliefs held about the ‘correctness’ of such

behaviours. The concept of emotional regulation appears in the SUEIT (Palmer & Stough,

55

2001) in the Emotional Control and Management8 factors, as well as in the Intrapersonal,

Stress Management and General Mood subscales9 of the EQ-i: S (Bar-On, 2002). Consider,

for example, that in individualistic cultures the identity is defined by personal goals and

achievement and emotion norms encourage emotions signaling independence, authenticity

and assertiveness (Triandis, 1994). In turn, Collectivism stresses that behavior is a function

of norms and duties imposed by the collective, hence the self is defined by one’s

relatedness to a social group whilst the views, needs and goals of the collective are stressed

(Triandis, 1988, 1994). Here, emotion norms promote emotions that signal interdependence

and endorse harmonious relationships (e.g., sympathy), as opposed to prescribing

concealments of emotions that may impede relationships with others (e.g., anger, pride).

Apart from specific influences on emotional regulation discussed below, this cultural

dimension might also influence the differential appropriateness of items and other subscales

in the two inventories under discussion. It may be argued, for example, that items with

content which focuses on behaviours like generally assisting / helping others, independence

in decision making and whether one generally cares about other people (see table 3), might

introduce cultural bias into these measures as such item content taps into typical

collectivistic values (and their associated behavioural manifestations). This could threaten

the construct validity of these measures.

Individualism / Collectivism

According to Triandis and Gelfand (1998) conflict inducing behaviours are minimised in

collectivistic cultures (e.g., Malaysia, Indonesia, Philippines) whilst individualistic cultures

(e.g., Australia, Canada, USA) will be more tolerant of individual deviance. Therefore,

fewer constraints that govern a wide range of emotion expression experiences in and among

members will occur. In addition, Kitayama and Markus (1994) inquire whether it might be

that anger is a highly pervasive, central and natural emotion in Western countries because

8 Emotional Control refers to how effectively emotional states experienced at work, such as anger, stress,

anxiety and frustration, are controlled. Emotional Management refers to the ability to manage positive and

negative emotions within both oneself and others (Palmer & Stough, 2001). 9 The Intrapersonal subscale assesses the respondent’s level of inner self awareness. High scores indicate

individuals who, for example, are in touch with and able to express their feelings, as well as are independent,

strong and confident in conveying their ideas and beliefs. Stress Management refers to the ability to withstand

stress without losing control or ‘falling apart’. General Mood assesses the ability to enjoy life, be content,

positive, hopeful and optimistic (Bar-On, 2002).

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of the emphasis on independence and the social norm of freely expressing internal

attributes, such as rights, goals or needs and because anger is most closely associated with

the blocking of these rights, goals and needs. Anger is therefore appropriate in situations

where personal goals or individual rights are threatened (Averill, 1982). In addition, anger

expression allows for restoring honour in this context (Cohen & Nisbett, 1994). In contrast,

Asian / Eastern countries stress interdependence among individuals (attending others’ needs

and goals) (Hofstede, 2001, Triandis, 1994) and therefore Kitayama and Markus (1994)

have asked whether it could be argued that anger is less common, natural and integrated

into the social life of individuals in non-Western cultures, or even that the two forms of

anger (in these two cultures) are distinct? This might have cultural bias implications for

items in EI assessment instruments (which measure Emotional Control) that contain the

word “anger”, e.g. “I find it easy to control my anger at work”.

Display rules

The linguistic implications of using a term like ‘anger’ in a self report instrument should

also be considered. For example, the standard English US translation for ‘anger’ in

Japanese is, ‘ikari’. It could be argued that if equivalent translation is assumed when this

term is included in self-report questionnaire items, these two references to ‘anger’ resemble

each other by sharing important elements such as autonomic arousal and the use of certain

face muscles. However, the exact set of participating components (e.g., instrumental

responses, inhibitory tendencies) related to ‘anger’ may vary widely across the two cultures

(Kitayama & Markus, 1994). The most prominent influence here is the use of display rules

in emotional regulation. Display rules serve as socially and culturally learned norms that

specify the appropriateness of displaying and expressing emotions and are known to be a

source of cultural variation in emotional phenomena (Ekman, 1972). According to Ekman

and Friesen (1975) display rules affect facial expressions of emotion in several ways. Facial

expressions of an emotion may be displayed without a corresponding feeling, it could mask

the presence of another inappropriate emotion, attenuate or enhance the apparent intensity

of a felt emotion, or even entirely mask or inhibit a felt emotion. Recently, Matsumoto et

al., (2005) found that when displaying fear, anger, or sadness, Japanese and Russian

respondents are inclined to soften the impression by adding a slight smile, indicating that

57

although they are distressed, ‘it isn’t really that bad’. Americans express their emotions

more visibly than do Japanese or Russian people. According to Matsumoto (1990, 1996)

moderate displays of anger are fairly common in the USA. The display of sadness or other

negative emotions are more appropriate towards friends and family, than acquaintances,

with the opposite being true in the Japanese culture. However, in Japan it is considered

appropriate to display anger towards subordinates, but any other display of anger is

considered crude and inappropriate (Matsumoto, 1996).

Moreover, Ellsworth (1994) asserts that it is not only a matter of the visible behaviour (e.g.,

behavioral manifestations of anger); cultures also seem to differ in their beliefs about the

appropriateness of even feeling certain emotions in certain contexts. For example, in

American culture, in most social contexts it is considered inappropriate for men to cry, and

also experience deep grief as strongly and frequently as women. Therefore, it could be

argued that each culture’s values about emotions and their expression may come to affect

the essential experience (and the expression and, ultimately, the definition) of that emotion

(Ellsworth, 1994).

Items like “I find it easy to control my anger at work”, “I overcome anger at work by

thinking through what’s causing it”, “At work I experience strong emotions that are hard to

control” and “I tend to explode with anger easily” are used to assess different components

of emotional regulation, in the EI measures under discussion. By including ‘anger’ as an

anchor and standard of cross-cultural comparison and generalisation in EI assessment, it

might be plausible to argue that an ethnocentric understanding of this emotion in emotional

regulation is enhanced and maintained. Furthermore, it might be plausible to argue that

respondents from countries with cultures with well defined display rules, might very

seldom ‘explode’ with anger. If these lines of reasoning are followed it should be noted that

items in this facet of EI measurement (emotional regulation) might be particularly

susceptible to cultural bias (which would influence the transportability of the instrument).

Uncertainty Avoidance

The Uncertainty Avoidance index refers to the degree a society is willing to accept and deal

with uncertainty (Hofstede, 2001). The essence of uncertainty is that it is a subjective

58

experience, and that extreme uncertainty creates intolerable anxiety (Hofstede, 2001).

Countries that score high on the Uncertainty Avoidance dimension (e.g., Italy; Hofstede,

2001) tend to be more expressive cultures. In such cultures it is socially acceptable to

express emotions, as anxiety is released through the showing of emotions through which

society has created outlets (Hofstede, 2001). In low Uncertainty Avoidance societies (e.g.

Malaysia, Hofstede, 2001) anxiety is released through passive relaxation, whilst such

cultures are characterised by lower expressiveness. The norm is wide social disapproval of

overly emotional or noisily behaviour. Items like, “I overcome anger at work by thinking

through what’s causing it”, “I find it easy to control my anger at work” and “At work I

experience strong emotions that are hard to control” therefore might contain cultural bias

when introduced into EI measures applied in different cultures.

Power Distance

Power Distance prescribes how societies deal with inequality between people (Hofstede,

2001). In high Power Distance societies (also termed a vertical society; Matsumoto, 1996),

for example Malaysia, the workplace relations between employer and employee are strictly

ruled and dependent on the decisions of the employer, power is centralised as much as

possible. Superiors and subordinates generally consider each other as existentially unequal

(Hofstede, 2001). Emotions and behaviours that advertise and reinforce status are

encouraged. In low Power Distance societies (horisontal societies; Matsumoto, 1996)

employers and employees work closely together, have equal status (even when education

levels differ) and democratic practices are applied. Here, general predictions about the

experience and expression, and hence regulation, of emotion is largely concerned with who

is expected to and allowed to express which emotions to whom. The notion that in high

Power Distance cultures dominant strong emotions (e.g., anger and pride) will be expressed

by superiors to subordinates (which will, in turn, express submissive emotions, e.g.

appreciation, shame), has been confirmed in two studies (Bochner & Hesketh, 1994;

Mondillon, Niedenthal, Brauer, Rohmann, Dalle, & Uchida, 2005). In Japan, for example,

it is appropriate for a high status person to express anger to subordinates, as this emotion

implies high status and a threat to hierarchy (Matsumoto, 1990), whilst the inverse is

known to be deeply offensive in Japanese culture (Matsumoto, 1996). A very clear

59

influence of display rules is noted in this culture, as felt emotions by group members /

subordinates (anger, sad, afraid) will be controlled to maintain group harmony. Once again,

items containing the word ‘anger’ (“I find it easy to control my anger at work” and “At

work I experience strong emotions that are hard to control”) may be susceptible to cultural

bias depending on which group the respondent belongs to. Items that refer to emotional

regulation directed at group members, rather than members from other groups that imply a

Power Distance effect, may have better face validity and not be so prone to cultural bias.

However, often 360 degree versions of EI tests, in which subordinates rate their leader’s EI,

are based on self report measures. An item like “The person I am rating finds it hard to

convey anxiety to colleagues” may be susceptible to bias as a leader in a high Power

Distance environment will most probably not convey anxiety to subordinates.

3.3.2 Emotional expression

Although convincing evidence for the universality of posed and spontaneous facial

emotional expression in early cross-cultural studies has been found (Ekman & Friesen,

1971; Ekman, 1972; Friesen, 1972), the concept of display rules (Ekman, 1972) and the

neuro-cultural theory of emotion proposed by Ekman and Friesen (1969) served in

acknowledging the presence of cultural variation in emotional expression. For example, a

study by Pittam, Gallois, Iwawaki, and Kroonenberg (1995) recently reported agreement

amongst Australian and Japanese respondents regarding the cultural differences in emotion

expressivity (i.e. perceived expressivity of people of difference cultural backgrounds).

More specifically, Japanese were consistently rated as less expressive than Australians by

all subjects (Pittam, Gallois, Iwawaki, & Kroonenberg, 1995), providing confirmation of

previous reported cultural and ethnic differences in intensity ratings of emotion expressions

(Matsumoto & Ekman, 1989, Matsumoto, 1993; Scherer, Wallbott, Matsumoto, Kudoh,

1988). Recent evidence suggests, furthermore, that these cultural and ethnic differences

also hold in Irish and Scandinavian American immigrant groups (Tsai & Chentsova-

Dutton, 2003) with Irish Americans consistently being more facially expressive (when

asked to relive target emotions like happiness, love and anger), than their Scandinavian

counterparts.

60

Emotional expression appears in the Bar-On (2002) EI model in the Intrapersonal

subscale10

. A typical item is: “It’s hard to express my intimate feelings”. In the SUEIT,

emotional expression appears in the compound Emotional Recognition and Expression11

factor. Typical items include: “When I’m anxious at work, I find it difficult to express this

to my colleagues”, “I can portray how I’m feeling to colleagues through my body

language”, “Colleagues know when I’m worried”, and “I find it hard to convey my anxiety

to colleagues”.


A study of the appropriateness of displaying emotions in different social situations

(individualistic versus collectivistic cultures), characterised by in-group (i.e., close family

and friends) and out-group (i.e., in public, casual acquaintances) members, was conducted

by Matsumoto (1990). Japanese subjects rated the display of anger to out-groups as more

appropriate than Americans. Americans, on the other hand, rated the display of disgust and

sadness to in-groups as more appropriate. To Americans, the display of happiness in public

was more befitting than to the Japanese. In general, items in EI tests tapping emotional

expression, refer to ‘others’, ‘other people’ or ‘colleagues’ (e.g. “It is hard for me to share

my deep feelings with others”) with no indication as to the relationship between the

expressor and perceiver. This may obscure effective measurement of emotional expression,

as respondents are not allowed to indicate when it is more appropriate to display / express

certain emotions to ‘others’ or ‘colleagues’. For example, if there is sufficient trust between

colleagues, then colleagues may become friends, view each other as part of an in-group,

and the expression of negative emotions within the American individualistic culture to

‘colleagues’ should be appropriate. For example, an item like “I can tell others when I am

angry with them” may then indicate effective emotionally intelligent behavior, which

should facilitate stress relief and lessen burnout. If the same scenario in Japanese culture

exists, it would not be deemed appropriate to display anger to friends (i.e. colleagues, the

in-group), rendering this item problematic. Hence, it is recommended that items of

10

The Intrapersonal subscales measures emotional self-awareness, as well as the ability to express feelings

and communicate emotional needs to others (Bar-On, 2002). 11

Emotional Recognition and Expression refers to the ability to identify one’s own feelings and emotional

states, as well as the ability to express those inner feelings to others (Palmer & Stough, 2001).

61

emotional expression should differentiate between in-group and out-group members to

more efficiently determine whether a respondent will appropriately display emotions (given

the cultural context) and subsequent emotionally intelligent behaviours.

3.3.3 Emotion recognition (judgment) in self and others

Classic studies in literate and preliterate cultures (Ekman & Friesen, 1971; Ekman, 1972;

Izard, 1971) provided evidence for the universality of the recognition of ‘basic’ emotions

(i.e. anger, disgust, fear, happiness, sadness, surprise) in facial expressions, at above-chance

accuracy. Critics of these studies have questioned the lack of ecological validity of the

stimuli used (Mesquita & Frijda, 1992) whilst others have focused on methodological

issues (e.g., use of forced choice repose formats; Russel, 1994). Others have argued that

cultural differences in the data of these original studies were overlooked as the interest of

the researchers was in exploring agreement, not disagreement (Matsumoto & Assar, 1992)

and therefore the examination of cultural differences in the same data has received more

attention recently (e.g. Mesquita & Frijda, 1992; Russel, 1994). For example, Huang, Tang,

Helmeste, Shioiri, and Smoeya (2001) report results (Japanese and Caucasian Facial

Expressions of Emotion photo set, Matsumoto & Ekman, 1988) that suggest major cross-

cultural differences between American and Asian viewers in identifying emotions from

static facial expressions, particularly when the posed emotion had negative consequences.

In addition, evidence for the cultural universality (Scherer, Banse, & Wallbott, 2001) and

differences (Van Bezooijen, Otto, & Heenan, 1983) in the recognition of emotions in vocal

affect has been reported. A recent meta-analysis by Elfbein and Ambady (2002) provided

compelling evidence to support an interactionist interpretation of emotional recognition.

Although evidence was found for the universality of certain core components of emotion,

evidence of an in-group advantage (i.e. understanding emotions more accurately when they

are expressed by members of the same national, ethnic or regional group) that accounts for

the cultural variability in emotion recognition was also reported. The meta-analysis was

based on the results of 97 studies (182 samples). More importantly, the results also suggest

that the, “…match between the cultural background of the expresser and judge is

important…” (Elfbein & Ambady, 2002, p.229) which is consistent with the theory of

cultural learning of emotional behaviour. Moreover, the in-group advantage was also noted

62

in groups that share the same native language (e.g., when English-speaking groups like

Scottish, Irish and New Zealanders judged the emotional expressions of Americans)

(Elfbein & Ambady, 2002).

Emotional recognition is a core facet of EI. It appears in the revised and refined Mayer and

Salovey model (1997) as ‘branch one’ termed Perception of Emotion (i.e. the ability to

perceive emotions in oneself and others, as well as in objects, art, stories and the like). In

the Bar-On (2002) model it appears in the Intrapersonal as well as the Interpersonal12

subscales, whilst being contained in the Emotional Recognition and Expression, as well as

Emotional Understanding13

factors in the SUEIT (Palmer & Stough, 2001). Items that tap

into different elements of emotional recognition (verbal and non-verbal) and that may be

influenced by an in-group advantage between the expressor and perceiver include: “It is

hard to determine how a colleague is feeling from their body language alone”, “I can tell

how a colleague is feeling by the tone of their voice”, “I can determine when a colleague’s

emotional reactions are inappropriate”, and “Colleagues’ facial expressions reveal a lot to

me about the way they are feeling”. Once again, it is important that the item content

explicitly differentiate between in- and out-group members.


As there is strong evidence to suggest cultural differences in emotional expression,

differences in interpreting emotional displays is likely to exist between cultures. Some have

suggested that due to an inward focus in individualistic cultures, individuals tend to project

their feelings onto others. In contrast, in collectivistic cultures the ability to be aware of the

impact of one’s emotions on others is emphasised (Cohen & Gunz, 2002). Moreover, when

estimating the intensity of facial expressions, Japanese tend to rate weak expressions as

constituting of stronger underlying emotions than when Americans rate the same facial

expression (Matsumoto, Consolacion, Yamada, Suzuki, Franklin, & Paul, 2002). In

12

The Interpersonal subscales assesses the extent to which an individual is able to establish cooperative,

constructive, and satisfying interpersonal relationship as well as the ability to understand and appreciate the

feelings of others (Bar-On, 2002). 13

The Understanding Emotions subscale measures the ability to identify and understand the emotions of

others and those that manifest in response to workplace environments (e.g. staff meetings) (Palmer & Stough,

2001).

63

addition, they rate both happiness and negative emotions of lesser intensity than their

American counterparts (Matsumoto & Ekman, 1989). These findings have been interpreted

in the light of the effect of display rules (Matsumoto et al., 2005). Americans may be more

prone to trusting the authenticity of the display, whilst the Japanese are inclined to infer

from a weak expression that even though a person feels a strong emotion, they partly

inhibited it.

A recent study by Masuda et al., (2005) on Japanese and American university students,

report that social context affects the perceived intensity of facial expression. The results

revealed that the perceived intensity of facial expressions (e.g. a central figure in a picture

displaying anger) judged by Japanese (collectivist culture), were more influenced by the

social context of emotions (e.g. others in a picture showed to the respondent, also displayed

anger), than the perceived intensity of expressions judged by individuals from

individualistic cultures.

Items such as, “Colleagues’ facial expressions reveal a lot to me about the way they are

feeling” and “I can determine when a colleague’s emotional reactions are inappropriate”

may be susceptible to bias due to the effect of display rules and values embedded in

collectivistic versus individualistic cultures, as evidenced by the aforementioned studies. In

addition, the item “I’m good at understanding the way other people feel” might be biased as

Japanese will rarely describe themselves as ‘above average’, no matter how skillful they

actually are (Kalat & Shiota, 2007).

3.4 Cross-cultural EI research: methodological issues

3.4.1 Convergence of two approaches

Different terminology for the two distinct approaches, i.e. etic – emic, cultural-specific –

cultural general and cultural – cross-cultural, in the research of emotion across cultures are

often used. The increasing emergence of the interactionist perspective permeating recent

theoretical models (Matsumoto, 1989; Russel, 1994; Scherer & Wallbott, 1994) that

account for universality and cultural variation in particular aspects of emotion, concur that

both these strategies/approaches are important for advancement in the field. Mirroring these

64

advances in emotion research, research on EI across cultures should aim to harness the

potential of both these approaches whilst avoiding known methodological pitfalls. Cross-

cultural research in EI to date is rudimentary and limited.

Item content taps behaviours related to… IND /

COL

PD UA Display

rules

being helpful towards others ×

being concerned about others / what happens to them ×

being more of a follower than a leader ×

independence in decision making ×

whether others perceive you as being assertive ×

easily exploding with anger ×

× ×

having problems to control / manage anger ×

× ×

finding it easy to control anger at work* ×

× ×

overcoming anger at work by thinking through what is causing it* ×

× ×

experiencing strong emotions at work which are hard to control* ×

× ×

finding it hard to control anxiety ×

× ×

expressing intimate feelings × ×

×

expressing feelings to colleagues when anxious* × ×

×

finding it difficult to convey anxiety to colleagues* × × × ×

whether colleagues know you are worried* × ×

×

determining when a colleague’s emotional reactions are inappropriate* ×

×

whether a colleague’s facial expressions reveal a lot to you about the way they

are feeling*

×

×

being happy / cheerful ×

finding it difficult to enjoy life ×

getting depressed ×

understanding how others feel ×

whether you can generate positive moods and emotions within yourself to get

over frustration at work*

×

when a colleague upsets you at work, whether you think through what the

person has said to find a solution to the problem*

×

Table 3 Theoretical framework of predicted cultural bias in (approximated) content of selected SUEIT and Bar-On EQ-i: S items

NOTE: A cross opposite the item indicates that, due to the respective cultural value dimensions (or display rules), the

item may be prone to display bias when included in EI measures that are used for cross-cultural assessment (e.g.

transporting a Western developed measure to a non-Western cultural context). IND / COL = Individualism /

Collectivism, PD = Power Distance, UA = Uncertainty Avoidance; An asterisk indicates which approximated item

content is from the SUEIT

65

Without such cross-cultural comparisons, psychological theory is confined to its own

cultural boundaries (Van de Vijver & Leung, 2001). In conducting cross-cultural EI

research, ethnocentrism in current EI theories (and associated measurement instruments)

may be reduced as the limitations of current theories are acknowledged, by seeking to

extend the data and theory through inclusion of other cultures (Berry et al., 2002). For

example, although scientific efforts addressing the matter of if and how EI can be

developed, is in infancy (e.g. Wong, Foo, Wang & Wong, 2007; Zeidner, Matthews,

Roberts, & McCann, 2003; Matthews, Roberts, & Zeidner, 2003) the utility of cross-

cultural knowledge to enhance our understanding in this EI domain, should not be

underestimated. If it is argued that more congruence (better fit) between personal and

cultural norms / beliefs enhance social interactions and adjustment (typical outcomes of

individuals with high EI) then a detailed understanding of how culture drives the norms of

emotionally intelligent behaviour (in a specific culture), is an essential basis for any

development intervention. This highlights the need for research studies on EI to be

conducted from within the cultural psychology framework. That is, where individual

behaviour (or psychology), and culture are viewed and studied as mutually constitutive

phenomena (Miller, 1997). Leung and Zhang (1995), for example, noted the need for

indigenous research and theorising, as well as research that integrates different cultural

perspectives as crucial to the establishment of more useful and universal psychological

theories.

3.4.2 Reframing bias and equivalence

Cross-cultural psychology holds the methodological ideal of transporting a procedure

established in one culture, with known psychometric properties, to one or more cultures

with the goal of making a cross-cultural comparison (Berry et al., 2002). The methodology

of the natural sciences is mirrored in these comparative studies, with the preference for

using standard instruments and a priori formulated hypotheses which is being tested in an

experimental or quasi-experimental fashion (Poortinga, 1997). However, the practice of

‘blindly exporting’ Western instruments to other cultures, without concern for the

appropriateness of the measures, could seriously impede theoretical advances (Cheung,

1996; Van de Vijver & Leung, 2001). In this chapter, various theoretical/conceptual

66

propositions that explore why current Western monocentered self report EI measures might

be susceptible to cultural bias, when exported to different cultures, have been suggested. As

discussed in chapter 2, bias (‘nuisance factors’) in data compromise the comparability

(equivalence) of scores across cultures (Van de Vijver, 2003). Traditionally, bias and

equivalence are treated, “… as dichotomous phenomena: data are biased or unbiased, and

our statistical analyses are geared at establishing which of the two possibilities applies”

(Van de Vijver & Leung, 2000, p. 47). However, this approach has impeded the

advancement of cross-cultural psychology. Future research should be aimed at the

quantification of bias and equivalence (Van de Vijver & Leung, 2000). In a similar vein,

Berry et al., (2002), for example, have noted that the uncovering of cultural bias should be

viewed as systematic information about cross-cultural differences, not to be equated with

measurement error.

The application of two strategies towards a more balanced treatment of bias and

equivalence is suggested (Van de Vijver & Leung, 2000). Firstly, suspected biasing factors

should be measured. For example, by including a social desirability measure together with

measures of the target construct in the design of a study, this type of bias may be confirmed

/ rejected. In addition, measuring contextual factors (i.e. including explanatory variables)

may assist in clarifying particular interpretations of cross-cultural differences. This

facilitates a movement away from post-hoc speculative unvalidated interpretations. A

second strategy is triangulation. Here bias is empirically scrutinised by explicitly applying a

monotrait-multimethod research design (Van de Vijver & Leung, 2000).

3.4.3 Applying measurement invariance in cross cultural EI research

In cross-cultural psychology, typical statistical techniques used to investigate structural

equivalence include Exploratory Factor Analysis, followed by target rotation and the

calculation of Tucker’s phi (Van de Vijver & Leung, 1997, Van de Vijver, 2003).

Obtaining evidence of structural equivalence allows the researcher to conclude that the

psychological constructs underlying the instrument, are identical (Van de Vijver & Leung,

1997). However, a less popular alternative is to utilise CFA, which allows for the testing of

a large set of hierarchically linked hypotheses of cross-cultural invariance (Van de Vijver &

67

Leung, 2001). More specifically, the use of multi-group CFA modeling (Jöreskog, 1971)

via Structural Equation Modeling (SEM) is especially functional and effective in

establishing cross-national measurement invariance (MI) (Steenkamp & Baumgartner,

1998). Here measurement equivalence (or invariance) is defined as the mathematical

equality of corresponding measurement parameters for a given factorially defined

construct, across two or more groups (Little, 1997).

More specifically, obtaining MI indicates that (Little, 1997, p.56):

“(1) the constructs under investigation are generalisable to each sociocultural context; (2) that the

least possible amount of sources of bias and error are present (e.g. cultural bias, translation errors); (3) it is

valid to assume that cultural influences have not impacted the construct’s underlying measurement features;

and (4) it is permissible to assess between-culture differences as mean-level, variance and covariance or

correlational effects.”

An increasing amount of researchers have applied measurement invariance procedures to

address aspects of the cross-cultural generalisability of psychological measures and their

associated models (e.g., Crockett, Shen, Randall, Russell, & Driscoll, 2005; Culhane,

Morera, & Watson, 2006; Riordan & Vandenberg, 1994) whilst others have focused on

conducting cross-group comparisons with ethnic groups or different nationalities (e.g.,

Darvasula, Andrews, Lyonski, & Netemeyer, 1993; Ghorbani et al., 2002) as the basis for

comparison, as opposed to gender or age (e.g., De Frias & Dixon, 2005; Gomez & Fisher,

2005).

With the recent resurgence of MI research, and increased application of the procedure, the

aim is often to uncover instrument invariance as a way to ensure that group differences on

the mean scores of a construct are meaningfully comparable. A different, and much less

frequent application of MI tests is applying it in a hypothesis testing context where a priori

conceptual and theoretical grounds (e.g., diversity in socio-cultural contexts) may be

identified as to why differences in psychological processes may exist (Vandenberg, 2002),

and using MI procedures to uncover such differences. For example, Cheung and Rensvold

(2002, p.252) recently argued that, “metric invariance…need not be seen merely as an

obstacle that must be surmounted before the equality of latent means can be assessed; rather

it should be seen as a source of potentially interesting and valuable information about how

68

different groups view the world…the same comment can be made with respect to any one

of the measurement invariance failures considered.” Therefore, in cross-cultural research

such an approach to MI testing requires that an absence of non-invariance should be

predicted a priori, based on the conceptual basis of differential cultural values (Chan,

2000), across the different groups that are being studied. This could be a powerful way to

explore the cultural specificity of a given psychological construct (e.g. EI), instead of

providing highly speculative, post hoc interpretations of why MI failed to hold over the

various groups under investigation.

In extending the use of MI tests as a hypothesis testing tool in the context of cross-cultural

research, the two studies that have attempted this (Cheung & Rensvold, 2000; Riordan &

Vandenberg, 1994) have been criticised for not operationalising the ‘trigger’ event - for

example, assuming that because a person belongs to a certain nationality, he or she

automatically prescribe to the national value system (e.g., US nationals prescribing to an

individualistic value system, Malaysian nationals prescribe to high Power Distance). As the

degree of prescription to these value systems was not directly operationalised the validity of

the results have been questioned (Vandenberg, 2002). However, it could be argued this is a

problem permeating almost all cross-cultural research. Recent empirical advances have

seen the development of individual level measures (Matsumoto, Weissman, Preston,

Brown, & Kupperbuscg, 1997; Triandis & Gelfand, 1998) and its related concept of

independent versus interdependent self-construals. However, individual level measures to

accurately measure the other dimensions of culture still need to be developed and should in

future be incorporated into studies as context variables to effectively unpack cross-cultural

comparisons (Matsumoto, 2004). Perhaps this limitation should be viewed through an

awareness that cross-cultural psychology, institutionalised in the 1960’s, is a relatively

‘young’ discipline (Jahoda & Krewer, 1997), with many methodological challenges still to

be resolved.

3.5 Conclusion

In this chapter current available cross-cultural EI research was reviewed. Weaknesses of

these studies were outlined. Attention was drawn to the need to examine cultural bias and

69

inequivalence in future culture-comparative EI studies. A review of key aspects of three

decades of emotions and culture research was presented. The possible influence of national

cultural value dimensions on EI operationalisation in a given culture (e.g. Western) was

outlined. The focus was on emotional regulation, expression and recognition as key aspects

of EI. Suggestions were made how cultures may differ on these aspects of EI. It was

argued that such differences may be a result of the fact that cultures differentially define

appropriate and adaptive emotionally intelligent behaviours. Hence, cultural differences in

values (e.g. Power Distance, Individualism) could introduce cultural bias into Western

cross-cultural EI measures when these are applied cross-culturally. Specific items were

predicted to be susceptible to cultural bias based on the item content which, for example,

taps some aspect of Individualism or Power Distance values. The presence of cultural bias

(construct, method or item bias) would express itself in the structural, metric or scalar

equivalence of the given instruments, when measures are transported from one culture (e.g.

Western) to another (e.g. non-Western). Hence, this chapter illustrated the need to

differentiate cultural bias from true construct variance when self-report measures of EI are

transported from one culture to another.

Chapter four will present empirical evidence of such an investigation that was conducted on

a self-report EI measure, the SUEIT (Palmer & Stough, 2001). Measurement invariance

procedures (SEM multi-group CFA analyses) were used to conduct the current

investigation. This dissertation illustrates how invariance research could be applied within a

cross-cultural research paradigm (as argued in section 3.4.3) to explicate cultural bias from

true construct variance, and better uncover the true nature of the EI construct over different

cultures.

70

CHAPTER 4

Cultural bias investigation of the SUEIT over various cross-national samples

4.1 Introduction

This dissertation investigated the generalisability and transportability of the SUEIT, a self-

report instrument for measuring EI (Palmer & Stough, 2001), to various samples

representing different national cultures. These national cultures have varying index scores

on the Hofstede (2001) cultural dimensions. Cultural bias could influence the

transportability of the instrument. The aim was to investigate various hypotheses regarding

construct, method and item bias over the different cross-national samples. In addition,

latent mean differences on the different dimensions of EI, across the cultural groups, were

also explored. The theoretical and practical implications of the results in the context of the

workplace are discussed.

4.2 Research questions and hypotheses

The central research question of this study can be formulated as follows: to what extent do

Hofstede (1980, 2001) cultural dimensions systematically influence the cross-cultural

transportability of self-report EI measures?

In chapter three it was argued that cultural context and value differences (e.g. Individualism

versus Collectivism, Power Distance, Uncertainty Avoidance) define the meaning of

behaviour in a given culture, and hence also associated reactions elicited with a given

psychometric test, that might render the scores derived thereof, inequivalent or culturally

biased (Berry et al., 2002).

Cultural distance (CD)1, a widely used construct in international business (e.g. foreign

investment expansion, entry mode choice), describes the extent to which cultures are

1 It should be noted that Shenkar (2001) has documented various problems related to the conceptual and

methodological properties of the cultural distance construct. For example, the assumptions of symmetry (in

distance), stability (over time), linearity (of impact on other variables) and causality have been discussed.

Problematic methodological properties include the assumption of corporate homogeneity (i.e. measures of

national culture implicitly assumes lack of corporate culture variance), the assumption of spatial homogeneity

(assuming uniformity within the national unit, whilst evidence suggests that intra-cultural variation explains

71

similar or different (Shenkar, 2001). Only three other studies have explicitly investigated

the effects of CD on psychometric test transportability. In 1986, Grubb and Ollendick

examined whether CD (i.e. sociocultural influences between major culture and subculture)

could account for group performance differences (Blacks and Whites) on IQ tests and

learning tasks. The results revealed that although both groups performed similar on the

learning tasks, differential performance was evident on the standardised IQ tests. The

authors proposed that this effect was due to the loading of cultural influences on the IQ

tests. In a subsequent analysis they demonstrated that, when cultural influences are

controlled for, differences in IQ performance were minimised.

More recently, Liu, Borg, and Spector (2004) and De Beuckelaer, Lievens and Swinnen

(2007) used Schwartz’s (1999) cultural theory to group countries in cultural regions and

test measurement equivalence (with multi-group CFA) of the data from the surveys within

and between these regions. Liu et al. (2004) reported high transportability of the German

Job Satisfaction Survey across countries with similar cultural and language backgrounds. In

addition they reported that in maximally dissimilar cultures (e.g. West Europe vs Far East)

a cultural distance effect on scale transportability was evident. The results from the De

Beuckelaer et al., (2007) study (over 25 countries), only partially supported Schwartz’s

theory. For example, evidence for scalar equivalence of the Global Organizational Survey

was only found in common English speaking regions. The results did not support the

hypothesis that more measurement equivalence would be evident among countries with

small cultural distances (i.e. groups in similar cultural regions), as opposed to those with

large cultural distance.

This research aimed at extending the limited work on the effect of CD on test

transportability by using the Hofstede (Hofstede, 1980, 2001) cultural dimensions

framework. In addition, this research is unique as it presents a CD ranking pattern in terms

of the dissimilarity between the host (e.g. Sri-Lanka) and home (e.g. Australian) countries

included in this study, to predict the effects on test transportability.

as much or more that inter-cultural variation), and the assumption of equivalence (not all cultural dimensions

are equally important in predicting outcome variables) (Shenkar, 2001).

72

Kogut and Singh operationalised CD by calculating a Euclidean distance in cultural value

dimensions between home and host countries (Kim & Gray, 2009). As is the norm, CD is

calculated in this study through an index (Kogut & Singh, 1988) from the Hofstede (1980,

2001) cultural dimensions. That is, a composite index was formed based on the deviation

along each of the four cultural dimensions (i.e. Power Distance, Uncertainty Avoidance,

Masculinity / Femininity, and Individualism / Collectivism) of each country (host country

where test is being used, e.g. Sri Lanka or South-Africa) from the Australian ranking (i.e.

home country where test was developed). Based on the Kogut and Singh (1988, p.422)

recommendation, “the deviations were corrected for differences in the variance of each

dimension, and then arithmetically averaged”. The index formula is as follows (Kogut &

Singh, 1988):

4

CDj = Σ {(Iij - Iia)2/Vi}/4,

i = j

where Iij stands for the index for the ith cultural dimension and jth country, Vi is the

variance of the index of the ith dimension, a indicates Australia, and CDj is cultural

difference of the jth country from Australia.

It is argued that CD will influence the transportability of the monocentered SUEIT. That is,

the more a particular culture is dissimilar to Australian culture (origin of the SUEIT) the

more pronounced the influence of culture will be on the transportability of the instrument –

and vice versa. Tables 4 - 6 present three calculations2 for CD from Australia, for the

cultures (countries) included in this study, based on the Hofstede dimensions (1980, 2001).

In addition, based on the CD results, a cultural distance ranking (CDR) pattern over all the

countries in terms of their cultural dissimilarity to Australia is provided. Two distinct

ranking patterns were identified. Pattern one (calculation based on all four Hofstede

dimensions) is presented in table 4. Pattern two (calculation based only on the Power

Distance and Individualism dimensions) is presented in table 5.

2 Due to the fact that no dimension scores were available for Sri Lanka (based on the original Hofstede study)

the Sri Lanka index scores were calculated by averaging the available scores of countries in the Southern Asia

cultural cluster (Malaysia, India, Philippines, Indonesia). Similarly, due to the fact that the SA non-White data

contained Coloured and African respondents, these dimension scores were calculated by averaging Black and

Coloured index scores obtained from the Sithole (2001) study.

73

Shenkar (2001) has noted that various studies have highlighted the fact that some cultural

dimensions might be more important than others, and that the aggregate CD measure may

provide false readings regarding meaningful cultural differences. For example, Kogut and

Singh (1988) themselves used Uncertainty Avoidance separately, in addition to the

aggregate CD index, in predicting choice of entry mode by foreign firms. Most of the

theoretical arguments, presented in chapter three, focused on the Individualism /

Collectivism, Power Distance and Uncertainty Avoidance dimensions. Hence, a separate

analysis for these three dimensions (in addition to all four cultural dimensions, table 4) was

conducted (table 6). The results revealed that the CDR pattern (1 = least dissimilar to

Australia, 6 = most dissimilar to Australia), was the same (i.e. pattern one) irrespective of

whether the three or four Hofstede dimensions were used. As expected, the smallest CD

was with the USA (CDR=1) and New Zealand (CDR=2), whilst the largest distance was

with Sri Lanka (CDR=6). The rankings of the other countries are less clear. SA White

(CDR=5) did not group with the other Anglo cultures. This may be due to the influence of

the extremely low Uncertainty Avoidance and Masculinity / Femininity scores in this

group. Conversely, SA non-White grouped with the Anglo cultures (CDR=3). This may be,

contrary to general beliefs, due to the fact that the Sithole (2001) study reported much

higher than expect Individualism scores for the African (Individualism / Collectivism = 73)

and Coloured (Individualism / Collectivism = 68) groups. This finding may be explained in

terms of social mobility theory (Cutright, 1968; Miller, 1960) which predicts that the

evolution of a Black middle class (due to the transformation of the SA society) may impact

the Black culture in moving from a collectivistic to individualistic cultural value

orientation.

However, another separate analysis was conducted for only Individualism / Collectivism

and Power Distance (table 5, CDR pattern two). This is because these two dimensions may

potentially be more important in the context of this study, in the light of the arguments put

forward in chapter three. The results revealed a different CDR pattern based on only these

two dimensions. A clear split between the strong individualistic / low Power Distance

Anglo cultures (USA: CDR = 1; New Zealand: CDR = 3; and SA White: CDR = 2) and

traditionally collectivistic / high Power Distance cultures (Sri Lanka: CDR = 6) and SA

Country

Cultural Dimension scores Difference squared / variance

CD

CDR PD UA IND / COL MAS / FEM PD UA IND / COL MAS / FEM

Australia 36 51 90 61 0 0 0 0 0 -

New Zealand 22 49 79 58 0.448 0.015 0.286 0.034 0.196 2

USA 40 46 91 62 0.036 0.099 0.002 0.003 0.035 1

Italy 50 75 76 70 0.448 2.301 0.464 0.310 0.881 4

SA White 42 22 78 21 0.082 3.360 0.341 6.134 2.479 5

SA non-White** 58 55 71 44 1.106 0.063 0.854 1.108 0.783 3

Sri Lanka* 88 42 30 54 6.182 0.323 8.525 0.187 3.804 6

Country


CD

CDR PD IND / COL PD IND / COL

Australia 36 90 0 0 0 -

New Zealand 22 79 0.448 0.286 0.367 3

USA 40 91 0.036 0.002 0.019 1

Italy 50 76 0.448 0.464 0.456 4

SA White 42 78 0.082 0.341 0.211 2

SA non-White 58 71 1.106 0.854 0.980 5

Sri Lanka 88 30 6.182 8.525 7.353 6

Country


CD CDR PD UA IND / COL PD UA IND / COL

Australia 36 51 90 0 0 0 0

New Zealand 22 49 79 0.448 0.015 0.286 0.250 2

USA 40 46 91 0.036 0.099 0.002 0.046 1

Italy 50 75 76 0.448 2.301 0.464 1.071 4

SA White 42 22 78 0.082 3.360 0.341 1.261 5

SA non-White 58 55 71 1.106 0.063 0.854 0.675 3

Sri Lanka 88 42 30 6.182 0.323 8.525 5.010 6

Table 4:

Cultural Distance calculated according to Kogut and Singh (1988) index based on all four Hofstede (1980, 2001) cultural dimensions (CDR pattern 1).

NOTE: PD = Power Distance; UA = Uncertainty Avoidance; IND / COL = Individualism / Collectivism; MAS / FEM = Masculinity / Femininity; CD = Cultural Distance; CDR =

Cultural Distance Ranking in terms of dissimilarity to Australia (1 = least, 6 = most);

Table 5:

Cultural Distance calculated according to Kogut and Singh (1988) index based on Individualism and Power Distance (Hofstede 1980, 2001) cultural dimensions (CDR pattern 2).

Table 6:

Cultural Distance calculated according to Kogut and Singh (1988) index based on Individualism, Power Distance and Uncertainty Avoidance (Hofstede

(1980, 2001) cultural dimensions (CDR pattern 1).

NOTE: PD = Power Distance; UA = Uncertainty Avoidance; IND / COL = Individualism / Collectivism; CD = Cultural Distance; CDR = Cultural

Distance Ranking in terms of dissimilarity to Australia (1 = least, 6 = most).

NOTE: PD = Power Distance; IND / COL = Individualism / Collectivism; CD = Cultural Distance; CDR = Cultural Distance Ranking

in terms of dissimilarity to Australia (1 = least, 6 = most).

75

non-White: CDR = 5) are supported by this result. Here it should be noted that even

though the SA non-White group has a relatively high Individualism score (71), it would

seem that traditional values in terms of Power Distance (e.g. respect for elders) still come

into play (second highest score on this dimension), even whilst there seems to be a move

towards Individualism in this group. For example, Sithole and Hall (2000) report that the

African culture of respect extends to business practices. That is, generally Africans extend

respect to those in authority (superiors), the elderly and visitors. “Africans avoid eye

contact with superiors as a sign of respect. They will give answers that they feel will please

strangers even when they are in complete disagreement” (Sithole, 2001, p. 20).

Three levels of bias have been introduced and discussed previously. These include:

construct, method and item bias (Van de Vijver & Leung, 1997). These three levels of bias

are related to three levels of equivalence (i.e. absence of bias).

More specifically: evidence for the presence of structural equivalence3 (i.e. a test measures

the same trait cross-culturally) indicates the absence of construct bias in the scores (Van

Herk et al., 2004). Item bias occurs when, “…one or a few items deviate from the

expectations about the response patterns in two cultural groups derived from other items in

the same instrument” (Van Herk et al., 2004, p.348) and is investigated with Item Response

Theory analyses. Chapter three contains the theoretical arguments related to specific items

in the SUEIT which could be susceptible to item bias based on the item content (table 3)4.

3 It should be noted that Cheung and Rensvold (2000) do not agree with this statement. According to them

(2000, p. 192), “…one type of noninvariance, known as construct bias (Van de Vijver & Poortinga, 1997)

cannot be detected statistically. Some constructs have wider scope in one culture that in another…a particular

set of items may be conceptually adequate for assessing a construct in one culture, inadequate in a second

culture, and yet display form invariance (structural equivalence) when compared using data from both

cultures.” Vandenberg (2002) alluded to a similar point in his article, Towards a further understanding of

measurement invariance methods and procedures, when he quoted personal communication with Rensvold on

this point. He admits, however, that in the absence of research evidence that have addressed this issue, it is not

know to what extent the “local nomological net” and strong presence of common method artifact as

characteristics of a given study, may influence the viability of MI testing procedures at this level. According

to Poortinga (personal communication, 2008) Cheung and Rensvold can be correct if only transported

measures are used. When measures of different societies are used, differences in representation almost

certainly should lead to differences in factor structures across cultures. 4 DIF may be ‘caused’ by numerous factors. These include cultural factors (e.g. ambiguities in interpretation,

low familiarity / appropriateness of item content, Van de Vijver & Tanzer, 2004) as well as other item

characteristics (e.g. item complexity or length, Budgell, Raju, & Quartetti, 1995). The framework of predicted

bias in specific SUEIT items is not an exhaustive list (presented throughout chapter three and in table 3). It

76

The MACS application (Chan, 2000) and 2-way analysis of variance (ANOVA) approach

proposed by Van de Vijver and Leung (1997) were used to uncover DIF in the SUEIT.

According to Van Herk et al., (2004) a lack of metric equivalence5 is likely due to method

bias. That is, the bias will affect most or all items to a similar extent, independent of the

construct studied. An example is an Acquiescence Response Style (ARS), which is also

known as agreement bias, i.e. a tendency to agree with questions, regardless of question

content (Johnson et al., 2005). Extreme Response Styles (ERS) is the tendency to use the

extreme ends of a rating scale (Cheung & Rensvold, 2000, Van Herk et al, 2000).

Demonstrating that a measure is free of ERS and ARS eliminates important alternative

explanations for observed cross-cultural differences. Finding between-group differences on

ERS or ARS needs, however, to be supported by the investigator’s understanding of the

research context6 (Cheung & Rensvold, 2000). According to Cheung and Rensvold (2000)

between group differences in ERS produce noninvariant factor loadings and intercepts that

can affect only a subset of items (nonuniform ERS) or all items (uniform ERS). ARS,

however, only influence intercepts. It can also be nonuniform and uniform. It is important

to note that testing for metric invariance can only uncover ERS. It cannot detect differences

in ARS. This is investigated by testing for intercept equality (scalar invariance).

Hamilton (1968) and Johnson, O’Rouke, Chavez, Sudman, Warnecke and Lacey (1997)

have provided some evidence to suggest that age and gender may be associated with

response artifacts (e.g. ERS). More recently, Johnson et al., (2005) reported significant age

and gender differences in ERS across 19 nations. In the current study, all the samples were

matched on age and gender. Hence, it could be argued that differences in ERS and ARS

could not be attributed to these two person-level background characteristics.

should be viewed as an illustration of examples of items that could be more prone to cultural bias. As the CD

with Australia increase (e.g. application of the SUEIT in Sri Lanka) more biased items should emerge. 5 Cheung and Rensvold (2000) note that Mullen (1995) refers to ARS as ‘scalar nonequivalence’. They hold

the view that cross-cultural ARS and ERS can be either nonuniform or uniform. Whereas Van Herk et al.

(2004) argue that a lack of metric invariance could be attributed to ARS or ERS or both (or even other forms

of method bias). Cheung and Rensvold (2000) hold a stricter view. According to them, lack of metric

invariance suggests differences in ERS. Lack of scalar invariance suggests differences in ARS. 6 This due to the fact that, “factorial noninvariance suggests but does not prove the existence of a between-

group difference in ERS” (Cheung & Rensvold, 2000, p. 199). The same is true for intercept noninvariance (it

suggests a between-group difference in ARS but cannot prove it).

77

Another form of method bias that could confound the MI results is the effect of

respondent’s verbal ability. For example, Marsh (1996) has demonstrated a negative

relationship between the observation of a negatively keyed item factor and verbal ability,

suggesting that individuals with less verbal skill may have difficulty reading negatively

keyed items, accurately, particularly those items with double negatives. Wong, Rindfleish,

and Burroughs (2003) have also identified cultural variability in the applicability of

reverse-worded Likert-type questions. They report such items to be problematic when

administered to East Asian, but not Western, populations. Gignac (2005) points out that the

vast majority of item level CFA analyses do not consider the possibility of item keyed

factors in the data. This might cause researchers to reject models due to unacceptable levels

of model fit, “despite the possibility that the only reason acceptable levels of model fit are

not achieved is simply because item keyed method factors were not modeled, in

conjunction with substantive factors” (Gignac, 2005, p. 165). Table 7 indicates in which

samples the influence of item keyed method effects will be investigated (positively and

negatively worded items). That is, samples in which respondents are bilingual and did not

complete the questionnaire in their mother tongue.

It should also be noted that, apart from ERS, ARS and the influence of verbal ability (i.e.

test language proficiencey) on the equivalence results, other forms of method bias (refer to

discussion in section 2.4.3) may also result in lack of metric invariance. That is, an absence

of ERS and ARS does not necessarily demonstrate absence of method bias, as these are not

the only forms of method bias. For the purposes of this study, however, it is proposed that

when a lack of metric invariance is found a first possible interpretation of this in terms of

method bias may be ERS or ARS (whichever is applicable) in samples where these

response styles are known to be a source of method bias (refer to table 7). This is because

method bias in this study may be viewed as a product of national (i.e. cultural) differences

in response styles. Hence, the measurement invariance results (metric invariance) will be

investigated further by calculating ERS and ARS indices for each of the samples per

subscale. The procedure and results are presented in section 4.5.6.

78

Australia, New Zealand, USA and the SA (White respondents) are categorised as Western

societies (GLOBE study, House et al., 2004). There is limited cultural variation between

these groups in terms of cultural dimensions (based on Power Distance and Individualism)

and hence the impact on the transportability of the instrument should be minimal. It is

hypothesised that construct, method and item bias would be low in these country results, as

opposed to countries / cultures more dissimilar to Australia. In addition, the presence of

method bias in these group results should be minimal as high Individualism (Hofstede,

1980) is associated with less ARS (Van Hemert et al., 2002) and not related to ERS

(Johnson et al., 2005). The only exception here may be the South African White data where

the influence of test completion in a second language may have resulted in method bias (see

table 7). However, even though limited cultural variation exists between these four

nationalities, the question of meaningful score comparability still needs to be addressed.

Hence, the configural and metric invariance of the SUEIT over these cross-national

samples was investigated.

Sri Lanka is a Southern Asian, non-Western society characterised by Collectivism and high

Power Distance. High Power Distance is associated with ERS (Johnson et al., 2005) and

more ARS7 (Van Hemert et al., 2002). Collectivism has also been found to be positively

related to ARS (Smith, 2004). All of the Sri Lankan respondents reported being bilingual,

having either Sinhala, Tamil or English as their first language. Hence, item keyed method

effects (as a source of method bias) may confound the measurement equivalence results.

This possible source of method bias will be investigated by modeling a measurement model

with positively and negatively keyed method effects as part of the SUEIT measurement

model.

All of the South African non-White respondents reported being bilingual. Method bias may

thus confound the MI results. Similar to the South African White and Sri Lanka analyses,

item keyed method effects will be investigated in this sample.

7 It should be noted that Johnson et al., (2005) found high Power Distance to be related to less ARS. This

finding was contrary to their expectations and hypothesis based on previous research findings reported here,

and in their study.

79

The possible effect of ERS, ARS and verbal ability on the cross-cultural comparisons

conducted in this study cannot be ignored. Table 7 provides an overview of the proposed

effect of ARS and ERS on the different cultures included in this study.

Culture Type of predicted method bias (based on previous research results)

ERS ARS Item keyed method effects

(Bilingual respondents)

SRI LANKA Yes (Sinhala, Tamil &

English)

Collectivistic n.a. Positively related (Smith,

2004)

High Power Distance Positively related

(Johnson et al, 2004)

Negatively related

(Johnson et al, 2004)

AUSTRALIA, USA, NEW

ZEALAND

No (English first

language)*

Individualistic Not related (Johnson et

al, 2004)

Negatively related

(Johnson et al., 2004)

-

Low Power Distance n.a. n.a.

ITALY No (answered

questionnaire in Italian)*


al, 2004)

n.a. -

Moderate Power Distance n.a. n.a.

SA WHITE Yes (Afrikaans, English)


al, 2004)

Negatively related



SA NON-WHITE Yes (Afrikaans, Xhosa,

English)


al, 2004)

Negatively related



Based on previous research results, the influence of method bias in terms of ERS and ARS

should be the most pronounced in the Sri Lanka data. The presence of method bias due to

verbal ability may be most pronounced in the South African White, South African non-

White and Sri Lanka data. The opposing ARS influences in Sri Lanka (positively related to

Collectivism and negatively related to Power Distance) should, theoretically at least, cancel

each other out. In individualistic societies, ERS is not normally observed. Hence, only ARS

and negatively keyed item effect hypotheses regarding method bias will be proposed and

investigated.

Table 7:

Predicted method bias for each cultural group included in this study.

*NOTE: Respondents in these samples answered the SUEIT in their mother tongue

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The following research questions and hypotheses are formulated:

Does construct bias influence the transportability of the SUEIT?

Hypothesis 1(a): The construct bias pattern of influence on the transportability of the

SUEIT will resemble CDR pattern 1 (described in tables 4 & 6).

Hypothesis 1(b): The construct bias pattern of influence on the transportability of the

SUEIT will resemble CDR pattern 2 (described in table 5).

Testing for configural invariance8 over the different sample groups allows for the

investigation of different degrees of structural equivalence9. It is proposed that better

configural invariance results will be obtained in cultures closer to Australian culture (e.g.

USA, New Zealand). Hence, the influence of construct bias on the transportability of the

SUEIT will be resembled in the fit indices obtained for each fully unconstrained multi-

group CFA analysis10

(e.g. Australia and USA, Australia and Sri Lanka) based on either of

the two proposed CDR patterns. That is, if only Power Distance and Individualism /

Collectivism are taken into account (table 5), the Australia and USA analyses will obtain

the best model fit, followed by the SA White, New Zealand, Italian, SA non-White, and Sri

Lanka multi-group results (pattern two), respectively. If all the Hofstede cultural

dimensions are taken into account (table 4), the multi-group fit results should resemble the

following pattern (from best to worse fit): USA, New Zealand, South Africa non-White,

Italy, South Africa White, and Sri Lanka (pattern one).

Does method bias influence the transportability of the SUEIT?

Hypothesis 2: Method bias (i.e. ERS) will have the most pronounced effect on the

transportability of the SUEIT when applied within the Sri Lankan sample.

8 The terms ‘configural invariance’ and ‘structural equivalence’ are used interchangeably in this study.

9 Structural equivalence was also investigated with the more traditional cross-cultural method of calculating

Tucker’s coefficient of agreement / Tucker’s Phi (Tucker, 1951). This coefficient has been developed to

indicate whether the pattern of factor loadings across items on a factor is the same across different groups.

According to Van de Vijver and Leung (1997) values below 0.90 point towards a lack of agreement, whilst

values higher that 0.95 are interpreted as evidence for the similarity of the factor matrices. Coefficients are

calculated separately for each subscale. In this study, a separate coefficient was calculated for each of the

SUEIT subscales for each of the separate countries, using Australia as the reference group. The results are

documented in appendix 4. Based on these analyses, no evidence of construct bias was evident. 10

This is the first step (i.e. testing for configural invariance) in die measurement invariance procedure

followed in this study (refer to section 4.3.2) for a more detailed discussion.

81

Hypothesis 3: Method bias (i.e. ERS) will not have an effect on the transportability of the

SUEIT when applied within the Australian, USA and New Zealand samples.

Hypothesis 4: Method bias (i.e. ARS) will have an effect on the transportability of the


Hypothesis 5: Method bias (i.e. ARS) will not have an effect on the transportability of the

SUEIT when applied within the Sri Lankan sample.

Hypothesis 6: Method bias (i.e. due to verbal ability / bilingualism of respondents) will

have the most pronounced effect on the transportability of the SUEIT when applied within

the Sri Lankan, South African White and non-White samples.

Systematic differences in response styles between countries may distort the validity of

cross-cultural comparisons. The investigation of hypotheses 2 – 6 would indicate the extent

to which method bias (i.e. due to verbal ability / bilingualism of respondents) may be

misconstrued as substantive differences in the latent construct (EI), when the SUEIT is used

for cross-cultural assessment. The results would also inform on the extent to which cultural

driven response styles (e.g. ARS or ERS) is a source of method bias in the current data.

Does item bias influence the transportability of the SUEIT?

Hypothesis 7: As the CD from Australia increases (i.e. CDR patterns 1 or 2), the higher the

probability that more biased items will emerge over the respective sample groups (e.g. least

number of biased items over the Australia and New Zealand / USA analyses, most with the

Australian and Sri Lanka analysis).

When subscale scores can be meaningfully compared11

across different cultures

(countries), do significant latent mean differences in emotional expression and regulation

as key aspects of emotionally intelligent behaviour exist over cultures (countries)? Are the

expected differences (i.e. significance and direction) congruent with theoretical arguments

in this regard?

The MACS DIF procedure (Chan, 2000) that was utilised to investigate item bias over the

various cultural groups (with Australia as the reference group) is described in section 4.3.4

11

That is, valid direct between groups comparisons are allowed.

82

(conducted with LISREL 8.8). For each series of analyses, the final outcome of the iterative

model fitting procedure was a partially constrained model for each of the multi-group

comparisons with Australia, in which the flagged item’s slopes and intercept equality

constraints were lifted (i.e. allowed to be freely estimated over the two groups). If at least

two item slopes and intercepts were found to be invariant (Hair et al., 2006) over the two

cultural groups, then partial metric and scalar invariance was assumed to exist. Hence,

latent mean differences for the given EI dimensions (subscale) could be estimated (based

on the partially constrained model with Australia as the reference group). This was done by

operationalising values for the means of the latent constructs (e.g. Emotional Expression,

Emotional Control). SEM programs like LISREL compare means only in a relative sense

(Hair et al., 2006). Hence, the vector of latent construct means (contained in the kappa

matrix) has to be fixed to zero in one group (Australia) to identify the model. The latent

mean is then freely estimated in the second group and the resulting value is interpreted as

how much higher or lower the latent construct mean is in the second group (e.g. New

Zealand, Italy) relative to the first group (Australia).

Theoretical arguments for expected latent mean differences on emotional regulation

(management and control) and expression is based on cultural value dimension differences

(for a summary, refer to table 8). For example, in individualistic cultures emotion norms

encourage emotions signaling independence, authenticity and assertiveness (Triandis,

1994). In turn, Collectivism stresses emotion norms that promote emotions that signal

interdependence and endorse harmonious relationships (e.g., sympathy), as opposed to

prescribing concealments of emotions that may impede relationships with others (e.g.,

anger, pride). Hence it may be argued that Sri Lanka as a collectivistic society will obtain

higher latent construct means on Emotional Management and Emotional Control and a

lower Emotional Expression latent construct mean, relative to Australia. On the other hand,

it is predicted that no significant differences in latent construct means will exist for similar

individualistic, low Power Distance societies like the USA and New Zealand, when latent

mean differences are estimated relative to Australia (individualistic society). Countries that

score high on the Uncertainty Avoidance dimension (e.g., Italy; Hofstede, 2001) tend to be

more expressive cultures. In such cultures it is socially acceptable to express emotions, as

83

anxiety is released through the showing of emotions through which society has created

outlets (Hofstede, 2001). It is, therefore, proposed that Italy will obtain a higher latent mean

on Emotional Expression and lower mean on Emotional Management and Emotional

Control, relative to Australia (lower / moderate Uncertainty Avoidance).

The following hypotheses12

regarding latent mean differences for three of the EI subscales

(Emotional Control, Emotional Management Self, and Emotional Expression) are

formulated:

Hypothesis 8: Sri Lanka as a Collectivistic, high Power Distance non-Western society will

obtain a significant higher latent mean score on Emotional Control than Australia, an

Individualistic, low Power Distance Western society.


obtain a significant higher latent mean score on Emotional Management Self than

Australia, an Individualistic, low Power Distance Western society.


obtain a significant lower latent mean score on Emotional Expression than Australia, an


Hypothesis 11: Italy, as an Individualistic, high Uncertainty Avoidance society, will obtain

a significant lower latent mean score on Emotional Management Self than Australia, an

Individualistic, moderate Uncertainty Avoidance Western society.


a significant lower latent mean score on Emotional Control than Australia, an



a significant higher latent mean score on Emotional Expression, than Australia, an


12

These hypotheses are formulated based on the theoretical arguments presented here and in chapter three.

This is not an exhaustive list of possible influences. Rather, only the strongest arguments are presented here

(i.e. where there are high / low differences in cultural dimension scores). However, latent mean differences

will be estimated and discussed for all the separate subscales over all the different countries (with Australia as

the reference group). The relative simplicity of these arguments is acknowledged as it is possible that an

interaction effect between the different cultural dimensions could have an opposing effect in determining

what is deemed to be emotionally intelligent behaviour in a given culture. No hypotheses are formulated for

the SA non-White group due to the moderate standing on the Power Distance and uncertainty avoidance

dimensions.

84

Hypothesis 14: There will not be significant latent mean score differences on the Emotional

Management Self, Emotional Control and Emotional Expression subscales between the

USA and Australia, as similar Western societies.


Management Self, Emotional Control and Emotional Expression subscales between New

Zealand and Australia, as similar Western societies.

Hypothesis 16: South Africa White, as an Individualistic, low Uncertainty Avoidance

society, will obtain a significant lower latent mean score on Emotional Expression, than

Australia, an Individualistic, moderate Uncertainty Avoidance Western society.


society, will obtain a significant higher latent mean score on Emotional Control, than



society, will obtain a significant higher latent mean score on Emotional Management Self,

than Australia, an Individualistic, moderate Uncertainty Avoidance Western society.

Cultural group Emotional Control Emotional

Management Self

Emotional

Expression

SRI LANKA**

Collectivistic (30) Higher Higher Lower

High Power Distance (88) Higher Higher Lower

Moderate Uncertainty Avoidance (42) - - -

USA

Individualistic (91) No sig. difference No sig. difference No sig. difference

Low Power Distance (40) No sig. difference No sig. difference No sig. difference


NEW ZEALAND




ITALY


Moderate Power Distance (50) - - -

High Uncertainty Avoidance (75) Lower Lower Higher

SA WHITE



Low Uncertainty Avoidance (22) Higher Higher Lower

SA NON-WHITE


Moderate Power Distance (58) - - -


Table 8

Predicted latent mean differences for each cultural group (relative to Australia*) on three SUEIT subscales,

based on Hofstede (1980, 2001) cultural dimensions (dimension scores indicated next to each dimension)

NOTE: *Dimension scores for Australia are Individualism (90), Power Distance (36), and Uncertainty Avoidance

(51). **The influence of display rules may also contribute to latent mean differences here (i.e. higher Emotional

Control, Emotional Management Self and lower Emotional Expression).

85

4.3 Data analytic strategies

4.3.1 Validity extension and generalisation

Validity generalisation refers to the process of identifying the best fitting baseline model

from a set of competing alternatives that replicated best across different populations

(Diamantopoulos & Siguaw, 2000). The validity generalisation procedure entails fitting

competing measurement models (e.g. five or seven factor structures) with a loose

replication strategy (validity extension) (Bentler, 1980). Hence a series of single group

CFA analyses to fit the original SUEIT five factor structure (Model 1, M1) (Palmer &

Stough, 2001), a seven factor (Model 2, M2), a modified five factor (Model 2a, M2a), as

well as a nine factor structure (Model 3, M3) (Gignac, 2005) on all the data included in this

study, was conducted (see table 11). The results of all the comparisons to identify the best

fitting baseline model (needed for the invariance analyses), are reported in sections 4.5.1

and 4.5.2.

4.3.2 Tests of MI: omnibus, configural, and metric tests of invariance

Once the best fitting baseline model has been identified (over all the groups) the next step

was to investigate the measurement invariance of the full instrument. The reason for these

analyses was twofold. First, the results would provide information on the level of

equivalence / invariance (i.e. configural, and/or metric) or lack thereof, over the various

countries (cultures) included in this study (with Australia as the reference group). A lack of

configural invariance would point towards construct bias. A lack of metric invariance could

point towards method bias. The practical implications of equivalence / invariance (or lack

thereof) will be discussed (i.e. the transportability of the instrument), in the light of the

current practice of using the measure in different cultures / countries. Secondly, the pattern

of invariance (or lack thereof) over all the countries (with Australia as the reference group)

will be interpreted in terms of the influence of CD on the transportability of the instrument.

For example, based on the CDR presented in tables 4 and 5, it is predicted that the USA

would obtain the highest level of equivalence and Sri Lanka the lowest.

The Vandenberg and Lance (2000) approach to MI was followed. First, the omnibus test for

the equality of variance-covariance matrices (Vandenberg & Lance, 2000), with the

86

condition that all parameter estimates are set to be equal across samples, is conducted. If

the null hypothesis (Σg = Σ

g’) of exact fit for the fully constrained model cannot be rejected,

MI is established. No further tests are necessary. If this is not the case a test of configural

invariance (“factor structure equivalence”, Hair et al., 2006; “weak factorial invariance”,

Horn & McArdle, 1992) is conducted next (fully unconstrained model). This model serves

as the baseline model for further analyses. That is, a series of nested models is compared

against it, using the chi-square difference test (Hair et al., 2006; Vandenberg & Lance,

2000). Establishing configural invariance provides evidence that the conceptualisation of

different constructs in different groups is the same (absence of construct bias), to the extent

of the data supporting the same number of factors and similar items associated with each

factor (Meredith, 1993). Configural invariance must be established in order for subsequent

tests to be meaningful (Byrne et al., 1989; Hair et al., 2006; Vandenberg, 2002). Failure to

establish configural invariance might point towards the level of abstraction of the measured

construct and more specifically to the fact that the perceptions of a particular construct

might be implicitly embedded in the cultural context of the respondents (Tayeb, 1994).

Moreover, it could also be argued that different groups attach disparate meanings to

different constructs; henceforth the manifest measures evoke different conceptual frames of

reference in each of the comparison groups (Riordan & Vandenberg, 1994; Vandenberg &

Lance, 2000).

After conducting the test of configural invariance, metric invariance13

is mostly advocated

as the next step (Byrne et al., 1989; Ghorbani et al., 2002; Hair et al., 2006; Steenkamp &

Baumgartner, 1998; Vandenberg & Lance, 2000). This test is effected by constraining the

factor loading of like items to be equal across groups (Steenkamp & Baumgartner, 1998;

Vandenberg & Lance, 2000). Some argue that full metric invariance is a rigorous test in

most contexts (Cheung & Rensvold, 2002; Hair et al., 2006; Horn, 1991).

13

Cheung and Rensvold (2002) distinguish between construct-level metric invariance [HΛ] and item-level

metric invariance [Hλ]. Construct-level metric invariance (i.e. the full metric invariance procedure conducted

in this study) exists when empirical evidence for the invariance of the overall strength of the relationship

between items and their underlying constructs over different groups, is obtained. Investigating item-level

metric invariance will uncover whether the strength of the relationship between each item and its underlying

construct is the same over the different groups. Here the authors indicate that a series of Hλ hypotheses can be

tested in order to detect the items causing overall noninvaraince (i.e. testing for partial invariance). To

investigate item-level metric invariance, the MACS DIF procedure of Chan (2000) was utilised (refer to

sections 4.4.4).

87

Hence, Byrne et al. (1989) proposed the approach of establishing partial invariance14

. For

the purposes of this study, however, full construct-level metric invariance (not partial

invariance) was investigated to provide insight into the practical implications of using the

SUEIT as a multidimensional EI measure in different cultural contexts.

Only if evidence of full (or at least partial) metric invariance is found, may scalar

invariance (full or partial) be investigated (Cheung & Rensvold, 2002; Steenkamp &

Baumgartner, 1998; Vandenberg & Lance, 2000). Scalar equivalence (also referred to as

“strong factorial invariance” by Meredith, 1993) exists when the intercept terms for each

measured variable are found to be invariant between different groups (Hair et al., 2006;

Vandenberg & Lance, 2000). It signifies that cross-national differences in the means of the

observed items may indeed be attributed to differences in the means of the underlying

construct(s) (Steenkamp & Baumgartner, 1998). Scalar invariance may only be investigated

if full metric invariance was found.

The following series of research questions will be investigated over all the countries, with

Australia as the reference group:

14

The reasoning underlying partial invariance is that for some, but not all manifest measures, invariance

restrictions may hold – hence the practice of relaxing invariance constraints where they do not hold, is

employed to control for partial measurement inequivalence. Hair et al. (2006, p. 823) specify that the level of

partial invariance needed to enable sufficient comparisons of relationships between groups, “… requires that

at least two loading estimates for each construct be equal between groups”. However, Vandenberg and Lance

(2000) recommend a rather conservative approach in executing partial invariance (e.g. relaxing constraints on

a strong theoretical basis as possible, only for a minority of indicators). Their relative modest endorsement of

this practice are well substantiated by evidence (cited in their review) that there seems to be a fair amount of

inconsistency in the application of statistical criteria for relaxing metric invariance constraints, leaving them

to conclude that the practice of invoking partial invariance constraints (up to the date of their review), “…has

been an exploratory, iterative, post hoc practice, and so it is subject to capitalisation on chance” (Vandenberg

& Lance, 2000, p. 37). Steenkamp and Baumgartner (1998) acknowledge that empirically driven model

respecification in the pursuit of testing for partial invariance should at all times be approached prudently.

They do propose that expected parameter changes (EPCs) and modification indices (MI) can be used in this

regard. They provide general guidelines for applying this technique. Critics of these practices (i.e.

investigating partial measurement invariance) argue that such models might not prove true invariance, even

suggesting that different inferences about group differences on latent means and variance may be generated

because of the different ways in which the model would be identified (e.g. Widaman & Reise, 1997).

88

Does the measurement model with the best generalisation potential display acceptable fit

on the data of the samples when fitted in a single multi-group confirmatory factor analysis

without any constraint on parameter equality (configural invariance)?

Does the measurement model with the best generalisation potential display acceptable fit

on the data of the samples when fitted in a single multi-group confirmatory factor analysis

with all freed parameters to be constrained equal (omnibus test – full measurement

invariance)?

Are the factor loadings of all the items invariant across the samples (full metric

invariance)?

Are the factor intercepts of all the items invariant across the samples (full scalar

invariance)?

4.3.3 Investigating method bias

To strengthen the finding that an absence of MI over the cultures may be attributed to

cultural differences between the groups studied, method bias (i.e. ERS and ARS response

styles, the effect of verbal ability on the structural equivalence results) will be investigated.

Two strategies will be followed here. First, ERS and ARS response indices will be

calculated to investigate response styles. Secondly, a series of CFAs with the purpose of

identifying possible method effects associated with item keying, will be conducted. If

evidence of method effects, i.e. method bias, is evident, the MI results, and conclusions

based on these results will be interpreted in light of these findings.

The SUEIT was translated into Italian (and back translated into English) before it was

administered to the Italian respondents. The instrument was administered in English (the

original language it was developed in) in all the other samples. However, as English might

be the second or third language of the majority of the respondents in the Sri Lanka and

South African datasets, the effect of method bias due to answering the questionnaire in a

second or third language, will be assessed over these groups.

South Africa has 11 national languages even though English is generally acknowledged as

the business language of the country (Sithole, 2001). Table 9 provides an overview of the

89

different languages and percentage of the population that converse in a given mother

tongue. The three languages mostly spoken in the Western Cape region (where the South

African data was collected) include Afrikaans, Xhosa and English. All of the respondents in

the South African samples (White and non-White) reported that they were bilingual. The

non-White respondents generally reported having Afrikaans or an African language as their

first language, with English as a second language. The White South African respondents

mostly have Afrikaans as a first language and English as a second language15

.

The three major languages spoken in Sri Lanka are Sinhala, Tamil and English. All of the

respondents in the Sri Lanka sample indicated that they were bilingual (i.e. they have either

Sinhala or Tamil as a first language and English as second language). However, when

asked to indicted which language they mostly converse in at work, 275 respondents

(46.5%) indicated that they mostly speak English, 188 respondents (31.8%) Sinhala, and 40

respondents (6.8%) indicated that they speak both languages (84 missing, 14.3%).

Language Total Population Percentage of Population

Afrikaans 5 811 547 14.4

English 3 457 467 8.6

IsiNdebele 586 961 1.5

IsiXhosa 7 196 118 17.9

IsiZulu 9 200 144 22.9

Sepedi 3 695 846 9.3

Sesotho 3 104 197 7.7

SiSwati 1 013 193 2.6

Setswana 3 301 774 8.3

Tshivenda 876 409 2.2

Other / Unspecified 1 866 844 4.6

Total 40 583 573 100.0

15

It should be noted that any self-report data regarding language in South Africa should be viewed with a

measure of caution. The language spoken by a particular subculture was often linked to the apartheid era

population group classification. Even within the post-apartheid context, for many ethnic groups the Afrikaans

language still bears the legacy of the apartheid regime (Wallis & Birt, 2003). For example, most Cape

Coloured individuals would converse mostly in Afrikaans, but when asked to report on their first language,

would indicate English. Due to this, it was decided to defer language group from the ethnic group information

(i.e. White South Africans is mostly Afrikaans speaking, and non-White respondents, i.e. Coloureds and

Africans mostly speak Afrikaans or an African language). In addition, the main rational underlying the

method bias analyses for language was that when a bilingual person answers a questionnaire in a second

language (only 8% of the South African population has English as a first language, refer to table 9) they may

have difficulty reading and understanding negatively keyed items, which could result in method bias and

influence test results.

Table 9

South African home languages (South African Department of Census and Statistics, 2001)

90

In order to investigate the effect of verbal ability / language proficiency on the structural

equivalence results, a model with negative and positively keyed method factors (M2b) was

fitted to the data of the countries with bilingual respondents (SA and Sri Lanka). The

procedure described in Van Herk et al., (2004) (refer to section 4.5.6) was used to compute

the ERS and ARS indices so as to investigate the prevalence of these response styles in the

different samples (cultural groups).

4.3.4 Investigating Differential Item Functioning (DIF)

As discussed in section 2.4.4, DIF refers to analyses that identify items for which members

of different subgroups with identical total test scores (or identical estimated true scores in

Item Response Theory, IRT, models) have a differing item performance. Two approaches

to investigate DIF were utilised. A similar pattern in the results was expected.

First, the 2-way analysis of variance (ANOVA) approach proposed by Van de Vijver and

Leung (1997) for detecting item bias was applied on all the items and datasets. This

approach is traditionally used in cross-cultural research. Second, a procedure for applying a

series of MACS models (Sörbom, 1974) was utilised to detect DIF. The procedure as

outlined by Chan (2000, pp. 180 - 182) is summarised in table 10.

91

4.4 Method

4.4.1 Measure: Swinburne University Emotional Intelligence Test (SUEIT, Palmer &

Stough, 2001)

A significant contribution to EI theory development was the development of a self-report

measure of EI for use in the workplace by researchers Palmer and Stough (2001). The

realisation that other models (e. g. Mayer et al., 2000; Bar-On, 1997, Goleman, 1998) of EI

share considerable overlap in both their theoretical content and structure led to the

execution of a factor analytic study to uncover the most definitive dimensions of the

construct. The authors included six of the most prevalent EI measures at the time, namely

Checking assumptions:

(1) Test assumption of uni-dimensionality of subscales by conducting a multiple-group CFA for each subscale,

allowing factor loadings and item intercepts to vary freely

(2) Conduct preliminary EFA and identify item with highest factor loading within each group (over all the groups)

Set of constraints imposed, includes:

(3) Identify reference indicator item and set loading to 1.0

(4) Set factor mean in first group fixed to 0 and allow factor mean in second group to always be freely estimated

(5) Always constrain reference indicator intercepts to be equal across both groups

(6) Always freely estimate the factor variance in all models in both groups

(7) Always freely estimate error variances in all models in both groups

Generic iterative model-fitting strategy:

(1) Fit fully constrained model (factor loadings, item intercepts constrained to be equal across groups)

(2) Flag items displaying DIF by examining size of MI (use chi-square tables to determine statistical significance of

MI value at a selected alpha value)

(3) Use Bonferonni correction to select alpha value at each step of iterative procedure

(4) Split sample and do validation procedure

Detecting non-uniform DIF (differential item discrimination across groups)

(1) Examine MI associated with factor loadings

(2) Identify largest significant MI value

(3) Remove between-group equality constraint on the flagged item’s factor loading

(4) Refit model whilst allowing flagged item to freely vary across groups

(5) Continue iterative procedure until largest MI associated with factor loading is no longer significant

Detecting uniform DIF (item difficulty parameter differs across groups)

(1) Examine MI associated with item intercepts

(2) Identify largest significant MI value

(3) Remove between group equality constraint on the flagged item’s intercept

(4) Refit model whilst allowing flagged item to freely vary across groups

(5) Continue iterative procedure until largest MI associated with item intercept is no longer significant

Confirm DIF of set of flagged items

(1) Estimate model with item parameters (i.e. factor loadings and factor intercepts) of flagged items freely

estimated, whilst fixing remaining item parameters to be equal across groups

(2) Compare with “fully constrained” model in which all item parameters (factor loadings and intercepts) were

constrained to be equal across groups

(3) Evaluate difference in model fit between two nested models (with a significant difference in model providing

support for the presence of DIF on the items that were flagged by the MI values in the iterative procedure).

Table 10

MACS procedure to test for DIF (Chan, 2000)

NOTE: Response on items using a polytomous ordered response format such as the Likert rating scales (used in the

SUEIT) are considered to be approximations of responses on a continuous line for the purposes of this procedure.

92

the MSCEIT (Mayer et al., 2000), the EQ-i (Bar-On, 1997), the TMMS (Salovey et al.,

1995), the TAS-20 (Bagby, Taylor & Parker, 1994), the scale by Schutte et al. (Schutte et

al., 1998) and the scale by Tett et al. (Tett et al., 1997). The result was an empirically-based

model of EI consisting of five factors (that account for 58% of the variance), (1) Emotional

Recognition and Expression (the ability to identify one’s own feelings and emotional states,

and the ability to express those inner feelings to others, sample item, “I can portray how I

am feeling to others through my body language”); (2) Understanding Others’ Emotions (the

ability to identify and understand the emotions of others and those that manifest in response

to workplace environments, staff meetings, artwork etc., sample item: “I can tell how

colleagues are feeling at work”); (3) Emotions Direct Cognition (the extent to which

emotions and emotional knowledge are incorporated in decision making and/or problem

solving, sample item: “My moods and emotions help me generate new ideas”); (4)

Emotional Management (the ability to manage positive and negative emotions within both

oneself and others, sample item: “I generate positive moods and emotions within myself to

get over being frustrated at work”); and (5) Emotional Control (how effectively emotional

states experienced at work, such as anger, stress, anxiety and frustration, are controlled,

sample item: “When I am anxious I can remain focused on what I am doing”).

Good internal consistency and test-retest reliability properties of the SUEIT have been

recorded in recent research (Gardner & Stough, 2002; Palmer, Gardner & Stough, 2003). In

terms of internal consistency, the SUEIT subscales have all been reported to have adequate

internal consistency, ranging from 0.70 for the Emotions Direct Cognition subscale to 0.91

for the Emotional Recognition and Expression subscale (Palmer et al., 2003). It has

furthermore been found that the SUEIT have high test-retest reliability over a one month

period with stability coefficients ranging from a low of 0.82 for the Emotional Recognition

and Expression subscale to a high of 0.92 for the Understanding Emotions subscale (Palmer

& Stough, 2001). Palmer et al., (2003) report a positive manifold of correlations between

the SUEIT subscales (average r = .32, p<.05), ranging from a low of r = 0.15 between the

Emotions Direct Cognition and Emotional Control subscales, to a high of r = 0.56 between

the Emotional Management and Emotional Control subscales. The authors conclude that

93

these correlations suggest that the sub-scales are distinct yet related facets, suggesting that

the SUEIT is measuring a unifactorial construct of EI.

4.4.2 The SUEIT measurement models

The measurement models that were tested consisted of the 64 measured variables (Xs), the

five (M1) (Palmer & Stough, 2001) / seven (M2) (Stough, personal communication, 2007) /

nine (M3) (Gignac, 2005) unmeasured latent factors (ξs), with single-headed arrows from

the ξs to Xs representing the proposed regression of the observed variable onto the latent

factors (λs). The latent factors were specified to be intercorrelated16

. Cross-loadings of

indicators across factors were not allowed in the model. In addition, one item with the

strongest loading on the respective scale (reference variable) was set to 1.0 to specify the

scale of each latent variable17

.

The exogenous 7 factor measurement model underlying the SUEIT (Stough, personal

communication, 2007) is graphically depicted in figure 1.

Based on the individual CFA results (for measurement models M1 – M3) reported below, a

modified five factor measurement model (M2a, the seven factor model without the EDC

and ER subscales18

) was proposed and tested as the model with the best validity

16

The theoretical reasoning for allowing this intercorrelation was threefold. First Emotional Management

Self, Emotional Management Others and Emotional Control reflect a common management of emotions facet

of EI. Secondly, Emotional Management Others and Understanding Emotions External both pertain to the

external. That is, the management and recognition / understanding of emotional information outside the self.

Lastly, it could be argued that Emotional Expression subsumes management and control. Hence, if the ability

to appropriately express emotions is lacking, then Emotional Management of self and others may be affected. 17

To establish which items should be used as reference variables, a series of Exploratory Factor Analyses

(Principle Axis Factoring) was conducted on every subscale per individual sample. The results were

compared so as to choose an item with a high loading over all the samples as a reference variable. 18

The decision to omit these subscales from the MI analyses was based on the consistent low reliabilities

obtained for the ER (see section 4.5.1) subscale due to the fact that it only retained two items after splitting

the original Emotional Recognition and Expression subscale (EREXP) into Emotional Recognition (ER) and

Emotional Expression (EE). For example the Cronbach Alpha values for this subscale ranged from 0.06 (SA

Non-White data) to 0.33 (SA White and Italy) to 0.60 in the USA sample. The EDC subscale was omitted due

the findings reported by Gignac (2005) which indicated that the items contained in this scale can conceptually

be separated into three categories. That is, “problem solving (4 items), idea generation (2 items), and decision

making (5 items), as well as a lone item pertaining to memory” (Gignac, 2005, p. 154). In addition, Gignac

(2005) reports evidence that the negatively and positively keyed items in the EDC subscale do not measure

exactly the same concept. In M2a Emotional Management Self refers to the ability to influence the emotions

within one’s self, i.e. being able to recover from an emotional set-back relatively quickly. Emotional

Management of Others is defined as the ability to influence the emotions of other individuals (Gignac, 2005).

94

generalisation potential, that is, the best fitting baseline model over all the countries. In

order to investigate method bias (due to verbal ability of the respondents), a model with

positive and negatively keyed method factors was also modelled with model M2a (referred

to as model M2b).

Original Five Factor

structure (Palmer & Stough,

2001) (M1)

Seven Factor structure

(Stough, personal

communication, 2007) (M2)

Modified Five

Factor structure

(M2a)

Nine Factor structure

without ‘g’ (Gignac, 2005,

p. 127) (M3)

1. Emotional Recognition

and Expression

1. Emotional Recognition 1. Emotional Recognition

2. Emotional Expression 1. Emotional

Expression

2. Personal Expression

3. Others Perception

2. Understanding Emotions

External


External

2. Understanding

Emotions

External


External

3. Emotions Direct

Cognition

4. Emotions Direct

Cognition

5. Emotions Direct

Cognition Positive

6. Emotions Direct

Cognition Negative

4. Emotional Management 5. Emotional Management

Self

3. Emotional

Management Self

7. Emotional Management

Self


Others

4. Emotional

Management

Others


Others

5. Emotional Control 7.Emotional Control 5. Emotional

Control

9. Emotional Control

4.4.3 Sampling procedure and data collection

Convenience sampling – as was utilised in this study - is most often employed in

generalisability studies in cross-cultural research due to availability and cost efficiency

(Van de Vijver & Leung, 1997, 2001). The use of this sampling procedure is known to lead

to an overrepresentation of affluent countries (Van de Vijver & Leung, 2001) as is the case

for this study. Only three of the six samples included in this study (i.e. South Africa White

and non-White, as well as Sri Lanka), was from less affluent countries. This is a limitation

of the research.

Table 11

Four different SUEIT factor structures (measurement models)

95

ER

ξ1

EE

ξ 2

UEX

ξ 3

EDC

ξ 4

EMS

ξ 5

X1

X2

X3

X4 - X10

X11

X12

X13 – X30

X31

X32

X33 - X13

X11

X44

X45 – X49

δ1 λ1,1

δ2 λ2,1

λ11,2

λ3,2

λ12,3

λ31,3

δ3

δ11

λ32,4

λ43,4

λ44,5

λ50,5

δ12

δ31

δ32

δ43

δ44

EMO

ξ 6

EC

ξ 7

-

-

-

-

-

-

X50

X51

X52 - X54

X55

X56

X56 – X63

X64

-

-

-

δ50

δ51

δ55

δ56

δ64

-

-

-

-

-

-

-

-

-

λ51,6

λ55,6

λ56,7

λ64,7

*

*

*

*

*

*

*

Figure 1

SUEIT seven factor model (M2) (Stough, personal communication, 2007)

96

All the data collection, with the exception of the Italian and part of the South African data,

was conducted through an online system over several years. Various co-workers in the

respective countries assisted with the setting up and running of projects. All the samples

were general workplace samples. A full description of the samples is provided in section

4.4.4.

4.4.4 Participants

Australian sample

The Australian database (n=3224) was provided by Genos Pty Ltd., a company that was

established in 2002, to commercialise the work of Dr Ben Palmer and Prof Con Stough on

the original Swinburne University Emotional Intelligence Test (Palmer & Stough, 2001).

The sample consisted of data from n=3224 adult participants, that completed the SUEIT

over a period of 4 years (2002 – 2005). The average age of the sample was 43.2 (SD=9.2).

Unfortunately, 51.4% of the sample did not report their age. Therefore, only the 1658 cases

where age was reported, was used when randomly matched samples for the invariance

analyses were drawn from the original Australian data. The sample consisted of 1473

females (45.7%) and 1692 males (52.5%). A total of 57 (1.8%) of participants did not

report their gender. A total of 1460 (45.3%) participants reported their educational

qualification obtained (54.7% missing). Three hundred and sixty three participants (11.3%)

reported having a post graduate qualification and 16% (516 participants) had obtained a

bachelors degree. Four hundred and fifty four participants (14.1%) reported having a post

secondary school certificate (diploma, certificate), and 3.9% (127 participants) had

completed a secondary school qualification.

The industries / services sectors included in the data ranged from financial and accounting;

education, training and development; local government, water and related services;

manufacturing and engineering; wholesale and retail; health and welfare to insurance

services. Table 12 provides an overview of the industries / services sectors represented in

each sample.

97

Australia New Zealand USA South-African

White

South-African

Non-White

Sri Lanka

% Freq % Freq % Freq % Freq % Freq % Freq Financial and

Accounting

services

10.6

343

-

-

-

-

12.4

36

6.2

21

1.4

8

Education,

training and

development

services

20.9

674

9.3

22

15.7

45

27.2

79

13.4

45

-

-

Health and

Welfare

11.1 357 5.9 14 14 40 26.2 76 22.8 77 - -

Manufacturing,

production,

engineering

6.3

202

10.2

24

-

-

-

-

-

-

68.1 403

Legal - - - - - - - - - - 3.5 21

Insurance - - - - - - 10.8 31 4.7 16 - -

IT systems, Tele-

communications

- - - - - - 12.4 36 46.9 158 2.2 13

Local government - - 25.4 60 6.6 19 - - - - - -

Wholesale and

retail

14 453 7.6 18 10.1 29 - - - - 1.5 9

Other 37.1 1195 17 40 13.2 38 7.6 22 4.5 15 23.3 138

Missing 0 0 24.6 58 40.4 116 3.4 10 1.5 5 0 0

Total 100 3224 100 236 100 287 100 290 100 337 100 592

New Zealand sample

The New Zealand data (n=236) was part of the Genos Pty Ltd SUEIT database. This

sample consisted of 116 male participants (49.2%) and 117 female participants (49.6%).

Three participants did not indicate their gender (1.3%). A total of 54 (22.8%) of the

participants did not report their age. For the rest of the sample (n=182), the mean age was

40.5 (SD=9.8). Forty six participants (19.5%) reported having a post graduate qualification

(MA or PhD), 21.6% (51 participants) an undergraduate qualification (BA or advanced

diploma), 14.4% (34 participants) a post secondary school certificate (diploma, certificate),

and 3.4% (8 participants) reported having a secondary school qualification. A total of 92

participants (39%) did not report any information on their qualification status. Table 12 lists

the five most prominent industries within which the data was gathered in New Zealand.

United States of America (USA) sample

This data was gathered by Genos Pty Ltd collaborators in the USA (n=287). A total of 13

participants (4.5%) did not report their gender. The average age of the sample was 44.9

(SD=13.0) and consisted of 100 males (34.8%) and 174 females (60.6%). Sixty four

participants did not report their age (16%). All the participants had obtained some form of

Table 12:

Industry representation per sample

NOTE: No information on industry representation for the Italian sample was available.

98

post secondary school education. More specifically, 4 participants (1.4%) had obtained a

secondary school certificate (diploma, certificate) and 111 participants (38.6%) reported

having obtained an undergraduate qualification (BA or advanced diploma). A notable large

portion of this sample (92 participants, 32.1%) had a post graduate qualification (72

participants a master’s degree, and 20 participants a PhD). Table 12 contains information

regarding the industries within which the data was collected.

Sri Lanka sample

This data (n=592) was collected from April – May 2007 with the Genos Pty Ltd online

system with the help of a Sri Lankan business consultant, currently living in Australia.

Most of the data was collected in a single large multinational manufacturing / production

company in Sri Lanka. The mean age of the sample was 33.5 (SD=7.6). Twenty two

participants (3.4%) did not report their age. The sample consisted of 412 male (69.6%) and

180 female participants (30.4%). Regarding education levels, 268 participants (45.3%) had

obtained an undergraduate qualification (BA or advanced diploma). A total of 166

participants (28%) had obtained a post secondary school certificate (diploma, certificate),

whilst 86 participants (14.5%) had obtained a senior secondary school qualification.

Information on the other industries that was included in the data is listed in table 12.

Italian sample

The Italian workplace sample (n=320) was collected by a co-worker in Rome, Italy (2004 –

2005). Unfortunately not other demographic information19

except for the age and gender

was provided. The mean age of the sample was 35.3 (SD=10.3) (none missing) and

consisted of 40.6% males (130 participants) and 59.4% females (190 participants). An

Italian translation equivalent version of the SUEIT was used to gather this data. In

accordance with cross-cultural assessment practices, the instrument was translated into

Italian and back-translated into English to ensure that an adequate translation was obtained.

19

The researcher employed various strategies to try and locate the original researcher. Unfortunately the

individual had left the tertiary educational institution and could not the contacted to receive more information

on the sample.

99

South African samples

The South African samples (non-White, n=337 and White, n=290) was collected over a

period of three years (2005 – 2007) in various organisations in the Western Cape region.

Electronic invitations were sent out with a request to follow a designated link to a website

where the questionnaire could be completed. Appendix 1 contains the cover letter that was

distributed in two large Western Cape universities, an IT company, as well as an insurance

company, to request participation in the study. The invitation was also sent out via an

electronic newsletter of the business school of the one university, as well as to a list of part

time adult students (all working) enrolled in a management program at a management

research institute situated at the one university. All of this data was collected via the online

system. Included in the South African database, is also data that was collected via paper-

and-pen test administration at various hospitals and a large call centre in the Western Cape.

Logistical limitations (access to the internet, computers) did not allow for online data

collection in these samples. Hence the difference in the administration procedure (online

versus paper-and-pen) should be noted as a limitation of the results related to this sample.

Although the South African sample is not representative of the South African population, it

does contain a sizable proportion of data from the three most prominent ethnic groups in

the Western Cape (i.e. White, Coloured and African individuals).

The average age of the White sample was 39.10 (SD=10.22) and consisted of 111 male

participants (38.3%) and 179 female participants (61.7%). Eleven participants (3.8%) did

not report their age. A total of 28 participants (9.6%) had obtained a secondary school

qualification, whilst 44 participants (15.1%) were in possession of a post secondary school

certificate (diploma, certificate). Twenty-one participants (7.2%) had obtained a bachelors

degree, whilst 119 participants (41%) held a post graduate qualification (Honours degree =

8.6%; Masters degree = 21.7% and Doctoral degree = 10.7%). A total of 78 participants

(26.9%) did not report information on their educational qualifications. Table 12 contains

information on the industries within which the participants were employed at the time of

the survey.

100

The non-White sample20

(121 Black / African participants, 35.9%; 216 Coloured

participants, 64.1%) consisted of 128 male participants (38%) and 200 female participants

(59.3%). Nine participants did not report their gender (2.7%). The mean age of the sample

was 32.82 (SD=8.93). Twenty four participants did not report their age (7.1%). About a

third of the sample reported having a secondary school educational qualification (125

participants, 37.1%), whilst 70 participants had obtained a post secondary school certificate

(20.8%). Forty participants reported having obtained a bachelors degree (11.9%) and 56

participants was in possession of a post graduate qualification (16.6%). More specifically,

25 participants (7.4%) had obtained an honours degree, 25 participants was in possession of

a masters degree (7.4%), whilst 6 participants had obtained a doctoral degree (1.8%).

Forty-six participants did not report any information on their educational qualification

(13.6%).

4.4.5 Data analytic procedure

Structural Equation Modeling (SEM) fit indices

With SEM analysis, various assumptions about the latent structure of a set of indicators are

evaluated by investigating various fit indices. Multiple criteria (Bentler, 1990;

Diamantapolous & Siguaw, 2000; Kelloway, 1998) were used in assessing the goodness-of-

fit of the datasets to the proposed models, as most researchers agree that SEM has no single

statistical test that best describes the ‘strength’ of the model’s predictions (Bollen & Long,

1993; Diamantapolous & Siguaw, 2000; Kelloway, 1998, Schumaker & Lomax, 1996).

More specifically, the following absolute and incremental fit indices are reported in order to

reach an integrative judgment on model fit: the Satorra-Bentler Chi-square (a correction for

the chi-square test statistic; Satorra & Bentler, 1988), Satorra-Bentler Chi-square/df ratio,

the Comparative Fit Index (CFI; Bentler, 1990), the Non-normed Fit Index / Tucker Lewis

Index (NNFI / TLI; Bentler & Bonnett, 1980), the Root Mean Square Error of

Approximation Index with confidence interval (RMSEA; Steiger, 1990), the Root Mean

Square Residual (RMR), as well as the standardized RMR (SRMR).

20

Due to sample size requirements for the SEM analyses (n>200), the Coloured and African participant’s data

was consolidated in one sample as the Black / African data would not have been enough to include on its own.

101

The chi-square statistic is a likelihood ratio test statistic (Byrne et al., 1989) which assesses

whether the unrestricted population variance / covariance matrix of the observed variables

is equal to the model-implied variance covariance matrix (Diamantopoulos & Siguaw,

2000; Kelloway, 1998; Mueller, 1996). The chi-square statistic is known to be affected by

sample size (Cheung & Rensvold, 2002; Diamantopoulos & Siguaw, 2000; Marsh, Balla, &

McDonald, 1988) providing a highly sensitive statistical test for large sample sizes. Any

deviation from perfect model fit is likely to result in a rejection of the hypothesised model

(Jöreskog, 1969) and hence obtaining non-significant test statistics in this instance is

unlikely (Kelloway, 1998). In addition, others have argued that the null hypothesis of

perfect fit may be considered to be rather unrealistic (Brown & Cudeck, 1993). Therefore,

alternative goodness-of-fit indices should be considered when evaluating model fit. The

CFI (Bentler, 1990), an incremental fit index, indexes the relative reduction in lack of fit as

estimated by the noncentral chi-square of a target model versus a baseline model (Hoyle &

Panter, 1995). The NNFI (also called the Tucker-Lewis Index, TLI), introduced by Tucker

and Lewis (1973) within the context of factor analysis, and discussed in SEM by Bentler

and Bonnet (1980), assesses the relative improvement of fit by comparing the target model

to a more conservative baseline (e.g. independence or null model) where no relations

among variables are specified. It is noted as a particularly suitable indicator of model fit in

cross-cultural measurement invariance studies (such as the present study) as the NNFI / TLI

rewards model parsimony, a characteristic which is of paramount importance in an

examination of nested models, a practice commonly utilised to assess invariance across

groups (Vandenberg & Lance, 2000). A brief perusal of a few similar cross-cultural,

generalisability or measurement invariance / equivalence studies (Bonaccio & Reeve, 2006;

Culhane et al., 2006; Ghorbani et al., 2002; Gomez & Fisher, 2005; Parker et al., 2005;

Shevlin & Adamson, 2005; Wang, Liu, Biddle, & Spray, 2005) revealed general agreement

in cutoff values for the aforementioned indices. Values of 0.9 and above for the CFI,

TLI/NNFI was referred to as indicating ‘well fitting models’ (Gomez & Fisher, 2005),

‘good model fit’ (Ghorbani et al., 2002) ‘acceptable’ fit (Wang et al., 2005) and an

‘appropriate lower bound of adequate fit’ (Bonaccio & Reeve, 2006). Others denote values

of > 0.95 as indicating ‘acceptable fit / good fit’ (Bonaccio & Reeve, 2006; Shevlin &

102

Adamson, 2005) citing Hu and Bentler (1999) as reference. Moreover, Wang et al (2005)

employed values of > 0.95 as indicative of ‘excellent fit’.

The RMSEA (Steiger, 1990) focuses on the discrepancy between the Σ and Σ(θ) per degree

of freedom (Diamantopoulos & Siguaw, 2000). It compensates for model complexity and

sample size. According to Brown and Cudeck (1993) values of 0.05 and less indicate close

fit, whilst Hu and Bentler (1999) recommend 0.06 as a cut-off for good-fitting models.

Values under 0.08 and between 0.08 and 0.10 indicate reasonable (Widaman & Reise,

1997), and mediocre fit respectively (Diamantopoulos & Siguaw, 2000). Values larger than

0.10 indicate poor fitting models (Brown & Cudeck, 1993). All the studies survey applied

these cutoff criteria. In addition, a test of close fit (in contrast to exact fit) is performed by

LISREL by testing Ho: RMSEA ≤ 0,05 against Ha: RMSEA > 0,05. That is, if a p-value for

close fit >0.05 is obtained, close fit has been achieved. The standardised RMR is a

summary measure of standardised residuals which represents the average differences

between the elements of the sample covariance matrix and the fitted covariance matrix

(Diamantopoulos & Siguaw, 2000). The SRMR (Jöreskog & Sörbom, 1981) is known to be

sensitive to model misspecification with values below 0.05 suggesting the model fits the

data very well (Kelloway, 1998). Values less than 0.08 are considered to be indicative of

acceptable model fit (Hu & Bentler, 1999) or well-fitting models (Vandenberg & Lance,

2000), a cutoff value also consistently applied over different studies. In summary, and in

accordance with the aforementioned criteria, cutoff values of 0.9021

for the CFI, TLI /

NNFI, 0.08 for the SRMR and 0.06 for the RMSEA22

will be employed to indicate well

fitting models in this study. The close fit value will also be interpreted.

Parameter estimation, variable type and item parcels

The purpose of parameter estimation is to find numerical values for the freed parameters of

the model that would minimise the difference between the observed and estimated /

21

In models with more that 30 observed variables (as is this case in this study) simulation research suggests

that with a sample of n<250 CFI cutoff values of 0.92 should be used. With n>250 and more than 30 observed

variables, a CFI cutoff of 0.90 should be applied (Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). Moreover,

more complex models with smaller samples may require somewhat less strict criteria for evaluation with

multiple fit indices (Sharma, Mukherjee, Jumar, & Sillon, 2005). 22

The <0.08 (reasonable fit) and <.10 (mediocre fit) RMSEA cut-off values were also utilised, especially

where models were compared over different countries, in order to ascertain relative fit between them.

103

reproduced sample variance / covariance matrices (Diamantopoulos & Siguaw, 2000).

LISREL 8.8 offers a number of different estimation methods, e.g. Generally Weighted

Least Squares (WLS), Diagonally Weighted Least Squares (DWLS), and Maximum

Likelihood (ML) (Diamantopoulos & Siguaw, 2000; Jöreskog & Sörbom, 1996). The

nature of the variables to be analysed, as well as the distributional properties of the data,

should be considered in order to decide on the appropriate estimation technique. For

example, the ML estimator assumes continuous multivariate normal observed variables

(Muthén & Kaplan, 1985). Questions pertaining to the most appropriate data analyses

strategy for this study, included: Which estimation technique should be used? Should the

correlation or covariance matrices be analysed? How should the variable type be defined?

Should item parcels be used? How will the distributional properties of the data impact the

results and what is an accepted strategy to deal with the implications of the sample

properties? What strategy should be used to deal with missing values?

As responses on the 64 individual SUEIT items are captured on a five point Likert scale, it

has been argued that the ordinal nature of the data requires that polychoric correlations and

the asymptotic covariance matrix should be analysed (Jöreskog, 2005). With this strategy,

Weighted Least Squares (WLS) or DWLS estimation techniques are used to derive the

model parameters as they fall within the category of asymptotic distribution-free estimators

(Diamantopoulos & Siguaw, 2000). Employing either of these estimation techniques,

require very large sample sizes (Diamantopoulos & Siguaw, 2000; Jöreskog & Sörbom,

1996) and extensive memory and processing time. This problem is exacerbated when multi-

group comparisons of measurement models on item level are conducted (as is the case in

this study). To overcome this, item parceling has increasingly been used (Bandalos &

Finney, 2001; Hagtvet & Nasser, 2004). In initial analyses in this study, the Jöreskog

(2005) methodology was applied (specifying the variable type as ordinal, using DWLS as

estimation technique). Due to the complexity of the model, the analyses were unstable and

required extensive memory and processing time. Upon an inquiry for a more practical

strategy from the developer of LISREL, Prof Jöreskog (personal communication, 2007)

advised the use of item parcels.

104

Item parcels is a heuristic approach applied to convert ordered categorical data to

continuous data (Bentler & Bonnett, 1980; Bentler & Chou, 1987; Nasser & Takahashi,

2003) in order to better meet the assumptions of ML estimation and improve non-normality

and variable to sample size ratio (e.g. Bonnacio & Reeve, 2006). Other cited advantages

range from increased stability of parameter estimates (Bandalos, Geske, &, Finney, 2001;

Bandalos, 2002) to lower skewness and kurtosis, as well as higher reliability and validity

for parcels than individual items (Nasser & Takahashi, 2003). Disadvantages include that

item parceling may improve model fit for all models, even if they are misspecified, as

parcel-based models tend to cancel out random and systematic error by aggregating across

these errors, improving model fit (Bandalos et al., 2001; Little, Cunningham, Shahar, &

Widaman, 2002).

Jöreskog’s recommendation of item parceling (personal communication, 2007) was

carefully considered in the light of the goals of this study. Two reasons are put forward for

why an item parceling strategy was not considered appropriate. First, the focus of this

research is on measurement invariance of the measurement model at the item level.

Utilising item parcels would not allow for the attainment of individual item parameter

estimates and possibly confound bias problems of individual items when parceled together.

Secondly, the results of a simulation study by Meade and Kroustalis (2005) illustrated that

when tests of metric invariance with item parcel indicators are conducted, “…pervasive and

considerable differences between groups can be masked” (p.7) by the parcels. The authors

conclude that when conducting MI analyses – as in the current study - items, and not

parcels, should be used as indicator variables.

A Monte Carlo study by Muthén and Kaplan (1985) investigated results derived from

different estimation techniques (i.e. ML, Generalized Least-Squares, Asymptotically

Distribution Free, Categorical variable methodoloy) when applied within a CFA SEM

framework on non-normal categorical variables, treated as interval scale (continuous) non-

normal variables. The results suggested that the practice of using ML estimation, where the

ordered five-category Likert scales are specified to be continuous and where these variables

are moderately skewed and kurtotic, is allowable as no severe distortion of the parameter,

105

standard error and chi-square estimates were observed. The authors concluded that,

“…these normal theory estimators (ML, Generalized Least-Squares) perform quite well

even with ordered categorical and moderately skewed/kurtotic variables” (Muthén &

Kaplan, 1985, p.187). Hence in this study the 64 observed variables were specified to be

continuous and ML was specified as the estimation technique.

ML assumes multivariate normality when fitting measurement models to continuous data

(Mels, 2003). The assumption of multivariate normality in a multi-group context is more

complex than with single population studies. According to Lubke and Muthen (2004, p.

515), “…results from robustness studies in a single homogeneous population concerning

the analysis of Likert-type data while violating the normality assumption, do not

necessarily carry over to the multiple group situation, and group comparisons may have

problems in addition to those encountered in single populations.” Results from their study

revealed that the source of unacceptable fit remains obscure in multi-group CFA of ordered

categorical data whilst incorrectly assuming multivariate normality (Lubke & Muthen,

2004). They concluded that it could not be known whether unfavourable measures of GOF

are due to a violation of ML assumptions, threshold differences across items that result in

structural differences or a result of the fact that the data are categorical and measures of

GOF based on the assumption of normally distributed data do not function properly (Lubke

& Muthen, 2004). In the light of these findings, the univariate and multivariate normality of

the indicator variables in all the samples were routinely inspected with PRELIS (Jöreskog

& Sörbom, 1996). Where the null hypothesis of univariate and multivariate normality was

rejected, Robust Maximum Likelihood (RML) was specified as the estimation technique

(Tabachnick & Fidell, 2001). This procedure produces the Satorra-Bentler chi-square

statistic (S-Bχ2) which tests for the closeness of fit between the unrestricted sample

covariance matrix and the postulated covariance matrix, after correcting for multivariate

normality. By employing this strategy, estimation problems of model testing related to a

lack of multivariate normality, e.g. underestimation of fit indices (Mels, 2003; West, Finch,

& Curran, 1995), are minimised.

106

Missing values: imputation by matching

Various strategies to treat missing values exist. Three categories can be distinguished:

deletion methods (list-wise and pair-wise deletion), model based (distributional) and non-

model based methods. The decision regarding the most suitable method for addressing the

problem of missing values in this study, should be based on the potential of the method to

enhance the inferential validity of the results (Raghunathan, 2004). List-wise deletion

(complete-case analysis) generally results in loss of large amounts of data which may

distort the representivity of the original sample (Raghunathan, 2004). Pair-wise deletion

could potentially produce invalid estimates due to the varying samples sizes used to

estimate parameters (Pigott, 2001). Model based methods like Full Information Maximum

Likelihood (FIML) and Multiple Imputations have clear advantages over traditional

deletion methods (Enders & Bandalos, 2001; Roth, 1994). For example, FIML has been

found to reduce bias that typically results from list- or pair-wise deletion of cases (Enders

& Bandalos, 2001) whilst Multiple Imputation corrects for bias by conducting several

imputations for each missing value (Sartori, Salvan, & Thomaseth, 2005). However, these

methods require data with a multivariate normal distribution (Pigott, 2001). In addition,

computational difficulties often render these methods impractical and cumbersome.

Although Multiple Imputation provides completed data sets after imputation (which is

needed to conduct the subsequent analysis in this study), FIML does not. Based on these

considerations, alternative non-model based methods of imputing missing values were

considered.

Non-model based methods (single mean imputation and imputation by matching) do not

require multivariate normal data. Single mean imputation is considered to be a fairly crude

method which is known to produce biased results (Pigott, 2001) as the variance of a given

variable is decreased by simply replacing all missing values (on the variable) with the mean

of all cases on that variable. Imputation by matching (similar response pattern imputation)

attempts to impute values from another case with similar observed values. Imputation does

not take place in the case where there is no observation with complete data on the set of

matching variables (Enders & Bandalos, 2001) and hence sample sizes could be slightly

attenuated. The main advantage of using this method is that the estimated data will preserve

107

deviations from the mean and the shape of the distribution (Little et al., 2002). Hence, this

method was chosen as the most appropriate strategy for addressing missing values in all the

samples with missing values. The procedure that was followed included to (a) obtain a

description of the exact number of missing values per item with PRELIS (in LISREL 8.8),

(b) identify matching variables (variables with no missing values), and (c) impute missing

values with PRELIS.

Most of the data was collected with the computerised online SUEIT system. This may have

restricted the number of missing values as the system does not allow a participant to skip a

question. Missing values would appear if the Internet connection was disconnected during

testing. The USA, Italian, Sri Lanka and South African White data contained no missing

values. Two cases in the New Zealand data had more than 50% missing values, and were

removed from the dataset (final n=234). Imputation by matching was used on the two

Australian samples (Australian n=3224, split sample), as well as the South African non-

White sample. In the Australia A sample (n=1618) 53.1% of the items had 5 or less missing

observations (one item had none). The remaining 46.9% of items had between 6 and 9

missing observations. In the Australia B sample (n=1618), three items had no missing

values, whilst 84.4% of the sample had seven or less observations. The remainder of items

(10) had all 8 or less missing observations. In the South African non-White sample (n=337)

21 items (32.8%) had no missing values. A total of 39 items (60.9%) had 5 or less missing

observations. Item 54 had the largest number of missing observations (7). After imputation,

the Australian A sample size was attenuated from n=1618 to n=1604, and Australia B from

n=1618 to n=1605. Imputation by matching on the SA non-White sample retained all cases

of the original sample (n=337).

Matching the samples

The validity of cross-cultural comparisons may be impeded if subjects from different

cultural groups are not matched on demographic characteristics (Van de Vijver & Leung,

1997). The practice of matching samples allows the researcher to rule out sample-specific

differences as alternative explanations for observed cultural differences. In this study the

invariance analyses entailed fitting six separate series of multi-group CFA models, for each

108

of the six countries (cultures) with Australia as the reference group (e.g. Australia and

USA, Australia and Sri Lanka etc). Therefore, comparable matched samples, for the

invariance analyses, were randomly drawn from the Australian sample (the 1658 cases

where age was reported). All the samples were matched on age and gender only23

. For

example, the USA sample (n=287) had a gender distribution of male = 40% and female =

60% and mean age of 44.89 (SD=13.01). The Australian matched sample (n=287) for the

Australia, USA MI analyses was therefore randomly drawn from the Australian sample

based on the USA sample characteristics. In all instances sample sizes were also matched,

as some GOF measures (e.g. chi-square statistic) is known to be affected by sample size

(Cheung & Rensvold, 2002; Diamantopoulos & Siguaw, 2000; Marsh et al., 1988). The

final list of matched samples for the MI analyses included (appendix 2 contains the

descriptive statistics for all raw data utilised in this study): Australia (n=287) and USA

(n=287), Australia (n=234) and New Zealand (n=234), Australia (n=320) and Italy (n=320),

Australia (n=337) and South Africa non-White (n=337), Australia (n=290) and South

Africa White (n=290), and Australia (n=587) and Sri Lanka (n=587).

4.5 Results

4.5.1 Results: Validity extension (loose replication)

The results for this section will be presented separately for each sample. First, the

descriptive statistics (means and standard deviations, univariate and multivariate normality

and reliability analysis) for models M2 and M2a are reported (appendix 3 contains the

descriptive statistics for models M1 and M3). This is followed by the single group CFA

analyses results, based on the selection of fit indices described in section 4.4.5. Results for

each of the separate factor structures (M1, M2, M2a and M3) are reported. Due to the fact

that model M2a was used for the invariance analyses, this section only reports the factor

loadings for this model per country. In accordance with recommendations by Cattell (1978)

as well as Ward, Kersh, and Waxmonsky (1998) items with loadings larger than |0.15| will

be considered as potentially significant. This fairly moderate criterion is applied as item

23

It is acknowledged that a there may be a range of potentially confounding variables (e.g. education, socio-

economic background) that may limit the comparability of the samples. Unfortunately, information on such

variables was not available over all the samples in order to empirically test their effects on the results.

109

level analyses tend to be more unreliable than subscales/facet level analyses (Gignac,

2005). In section 4.5.2 a discussion of the results of all the samples is presented.

4.5.1.1 Australian results

The Australian (n=3224) sample was randomly split with SPSS (version 15) into two

samples (sample A=1618, sample B=1618) to allow for cross-validation of the Australian

results. Missing values were imputed (multiple imputation) with PRELIS 2.8 (Jöreskog &

Sörbom, 2002) resulting in a slight decrease of both the sample sizes (sample: A=1604,

sample: B=1605). The age and gender distributions for the analysis sample (sample A:

M=43.12, SD= 9.01, 47.2% missing; male=51.7%, female: 46.3%, 2% missing) and the

holdout / calibration sample (sample B: M=43.28, SD= 9.47, 49.5% missing;

male=53.27%, female: 45.1%, 1.7% missing) were comparable. PRELIS 2.8 (Jöreskog &

Sörbom, 2002) was used to evaluate the univariate and multivariate normality of the 64

observed variables (items). In both samples the null hypothesis of multivariate normality

was rejected (skewness and kurtosis in sample A: χ2=10559.9, p=0.000, n=1604; sample B:

χ2=18899.3, p=0.000, n=1605). Robust Maximum Likelihood Estimation was employed to

derive model parameter estimates. The internal reliabilities, means and standard deviations

for the subscales of M2 and M2a over the two samples are reported in table 13 (refer to

appendix 3 for summary statistics of M1 and M3).

Australia (n=1604) Australia (n=1605)

Scale M SD α* N of

Items

M SD α* N of

Items

Emotional Recognition 7.93 1.12 0.60 2 7.99 1.10 0.54 2

Emotional Expression 31.49 4.63 0.81 9 31.71 4.51 0.79 9

Understanding Emotions External 77.67 7.77 0.90 20 78.25 7.50 0.89 20

Emotions Direct Cognition 35.09 6.56 0.86 12 35.20 6.340 0.85 12

Emotional Management Self 25.92 3.72 0.84 7 25.96 3.71 0.83 7

Emotional Management Others 17.28 2.52 0.67 5 17.43 2.51 0.65 5

Emotional Control 33.60 4.22 0.80 9 33.53 4.23 0.80 9

Total EI (M2) 230.08 20.52 0.71 7 230.08 20.52 0.73 7

Total EI (M2a)** 185.96 16.99 0.73 5 186.89 16.97 0.75 5

Table 13

Subscale internal reliabilities, means and standard deviations for Australian split samples for models M2 and M2a

*Alphas calculated after missing values were imputed.

**Model M2a mean scores were calculated by omitting ER and EDC subscales.

110

The results of the single group CFA analyses conducted with LISREL 8.8 (Jöreskog &

Sörbom, 2002) for the four different measurement models over both the samples are

reported in table 14. Overall, the data fit very well to models M2, M2a and M3 (i.e.

RMSEA <0.06, SRMR<0.08). The original five factor structure (M1) obtained acceptable

to good model fit with a RMSEA <0.06 and a SRMR of <0.09. Evidence for close fit

emerged for the nine factor model, not obtained with the other models. All the incremental

fit statistics were above 0.90. The increase in the NNFI and CFI indices, as well as decrease

in RMSEA values as the latent factors being modeled, increases, clearly suggests that more

latent factors underlie the 64 items, than the original hypothesised five factor model. More

specifically, the current results provide clear evidence that replicate the finding of nine

substantive factors within the SUEIT (Gignac, 2005). All the factor loadings for both

samples were statistically significant, ranging from 0.33 (item 23) to 0.75 (item 57) (sample

A) and 0.35 – 0.74 (sample B) (same items). The only item that obtained a loading below

0.30 in both samples (0.26, sample A and 0.28, sample B) is item 37 (“Colleagues know

when I’m worried”).

Model χ2 S-Bχ2 df S-Bχ2/

df

NNFI CFI RMSR SRMR RMSEA (CI) P

(close) M1

Australia A

15327.25*

13298.53*

1942

6.85

0.93

0.93

0.051

0.087

0.060

(0.059; 0.061)

0.00

Australia B 15289.64* 12880.06* 1942 6.63 0.92 0.93 0.050 0.085 0.059

(0.058; 0.060)

0.00

M2

Australia A

12990.58*

11078.90*

1931

5.74

0.94

0.94

0.044

0.074

0.054

(0.053; 0.055)

0.00

Australia B 13118.13* 10649.12* 1931 5.51 0.94 0.94 0.044 0.074 0.053

(0.052; 0.054)

0.00

M2a

Australia A

9104.71*

7676.40*

1165

6.59

0.94

0.94

0.042

0.074

0.059

(0.058; 0.060)

0.00

Australia B 8437.88* 6823.30* 1165 5.86 0.95 0.95 0.040 0.071 0.055

(0.054; 0.056)

0.00

M3

Australia A

8140.33*

7087.53*

1916

3.69

0.97

0.97

0.031

0.054

0.041

(0.040; 0.042)

1.00

Australia B 8423.47* 7079.35* 1916 3.69 0.96 0.97 0.032 0.055 0.041

(0.040; 0.042)

1.00

Table 14

Goodness-of-fit statistics results of the single-group SUEIT CFAs for Australia A (n=1604) and Australia B (n=1605)

samples

Note: S-Bχ2, Sattora-Bentler Scaled Chi-square; NNFI, non-normed fit index; CFI, comparative fit index; RMSR, root

mean squared residuals; SRMR, standardised root mean residual; RMSEA, root mean square error of approximation

with 90% confidence interval

* p < 0.001

111

Item EE

Aus A

Aus B

UEX

Aus A

Aus B

EMS

Aus A

Aus B

EMO

Aus A

Aus B

EC

Aus A

Aus B

7 0.74 0.72

10 0.38 0.36

14 0.74 0.69

20 0.63 0.64

26 0.37 0.40

32 0.59 0.57

37 0.26 0.28

42 0.74 0.70

61 0.47 0.46

1 0.53 0.57

5 0.56 0.55

8 0.59 0.54

13 0.53 0.48

16 0.60 0.57

17 0.62 0.59

22 0.65 0.61

23 0.35 0.33

27 0.66 0.59

29 0.50 0.50

33 0.51 0.49

34 0.67 0.64

38 0.46 0.43

43 0.53 0.51

45 0.58 0.60

48 0.63 0.60

52 0.51 0.57

56 0.59 0.57

59 0.65 0.64

63 0.55 0.47

2 0.54 0.53

11 0.65 0.61

49 0.75 0.74

54 0.74 0.73

57 0.75 0.74

60 0.70 0.73

64 0.44 0.43

15 0.64 0.60

28 0.54 0.53

39 0.63 0.58

44 0.57 0.52

58 0.38 0.40

4 0.55 0.55

6 0.55 0.52

9 0.66 0.63

19 0.50 0.48

25 0.47 0.48

31 0.71 0.71

36 0.42 0.45

41 0.64 0.66

46 0.56 0.55

Table 15

Summary of RML completely standardised parameter estimates for model M2a for Australia A and B

NOTE: All factor loadings are statistically significant (p<0.05).

112

4.5.1.2 New Zealand results

Table 16 contains the descriptive statistics for the subscales of models M2 and M2a (refer

to appendix 3 for descriptive statistics on M1 and M3). The internal reliability results

closely matched the results obtained in the Australian samples with alpha values ranging

from 0.77 to 0.88, expect for the two subscales with the least number of items (i.e.

Emotional Recognition, α=0.57 and Emotional Management Others, α=0.63) which still

obtained reasonable results (given the small number of items). Investigation of the

univariate and multivariate normality of the 64 observed variables (items) with PRELIS 2.8

(Jöreskog & Sörbom, 2002) revealed the rejection of the null hypothesis for both the

univariate and multivariate normality (multivariate normality results - skewness and

kurtosis: χ2=1294.95 p<0.05). The Satorra-Bentler chi-square statistic is reported. Table 17

contains the results of the single group CFA analyses for the four measurement models.

Table 18 contains the factor loadings for model M2a. All the loadings were statistically

significant (p<0.05).

New Zealand (n=234)

Scale M SD α* N of Items

Emotional Recognition 8.08 1.08 0.57 2

Emotional Expression 32.06 4.11 0.77 9

Understanding Emotions External 78.64 6.90 0.88 20

Emotions Direct Cognition 36.39 5.86 0.84 12

Emotional Management Self 26.05 3.50 0.82 7

Emotional Management Others 17.30 2.34 0.63 5

Emotional Control 33.76 3.83 0.77 9

Total EI (M2) 232.27 18.07 0.69 7

Total EI (M2a)** 187.81 14.89 0.70 5


**Model M2a mean scores was calculated by omitting ER and EDC subscales.

Table 16

Subscale internal reliabilities, means and standard deviations for New-Zealand sample (n=234) models M2 and M2a

113


df


(close)

M1 3955.71* 3545.37* 1942 1.83 0.90 0.91 0.054 0.10 0.060

(0.056; 0.063)

0.00

M2 3588.63* 3214.64* 1931 1.66 0.92 0.93 0.050 0.093 0.053

(0.050; 0.057)

0.043

M2a 2179.36* 1925.82* 1165 1.65 0.94 0.94 0.045 0.089 0.053

(0.049; 0.057)

0.12

M3 2840.37* 2567.67* 1916 1.34 0.96 0.96 0.038 0.073 0.038

(0.034; 0.042)

1.00

A similar pattern for the model fit results, in comparison with the Australian results,

emerged. In comparison with the other models, model M1 obtained the worse results.

Although a RMSEA of 0.06 was achieved for this model, the SRMR of 0.1 raises some

doubts regarding the adequacy of the five factor model fit. The NNFI and CFA values

further confirmed this, by obtaining values on and just above the 0.90 cutoff. A notable

improvement in model fit is evident with the splitting of the original Emotional

Recognition and Expression, as well as Emotional Management factors, both into two

factors (i.e. Emotional Recognition, Emotional Expression, Emotional Management Self

and Emotional Management Others). Models M2a and M3 obtained NNFI and CFI values

close to, and above 0.95. In addition, close fit was achieved as the H0: RMSEA ≤ 0.05

could not be rejected given a p-value for the test of close fit (RMSEA < 0.05) equal to 0.12

(M2a) and 1.00 (M3) in both the models. It should be noted that, even with a RMSEA

value well below the 0.06 cutoff (RMSEA = 0.038) for the best fitting model (M3), an

SRMR value of just below 0.08 (0.074) was achieved. This is at odds with the Australian

results, where the low RMSEA values (0.041), obtained for model M3 was further

confirmed with a SRMR values equal to 0.05 (indicating excellent fit, Kelloway, 1998).

Item 14 (“When I’m anxious at work I find it difficult to express this to my colleagues”)

obtained the highest factor loading (0.74). Similar to the Australian results, item 37

(“Colleagues know when I’m worried”) obtained the lowest loading (0.25).




* p < 0.001

Table 17

Goodness-of-fit statistics results of the single-group SUEIT CFAs for New-Zealand sample (n=234).

114

Item EE UEX EMS EMO EC

7 0.71

10 0.31

14 0.74

20 0.57

26 0.30

32 0.45

37 0.25

42 0.70

61 0.49

1 0.53

5 0.58

8 0.52

13 0.52

16 0.44

17 0.58

22 0.60

23 0.37

27 0.54

29 0.55

33 0.43

34 0.71

38 0.28

43 0.53

45 0.50

48 0.58

52 0.50

56 0.58

59 0.56

63 0.47

2 0.53

11 0.68

49 0.67

54 0.73

57 0.72

60 0.69

64 0.41

15 0.59

28 0.53

39 0.53

44 0.58

58 0.33

4 0.44

6 0.54

9 0.58

19 0.45

25 0.50

31 0.69

36 0.44

41 0.61

46 0.46

Table 18

Summary of RML completely standardised parameter estimates for model M2a for New Zealand


115

4.5.1.3 USA results

The means, standard deviations, and Cronbach Alpha’s per subscale for models M2 and

M2a, are presented in table 19 (refer to appendix 3 for descriptive statistics of M1 and M3).

Once again, both the null hypotheses of univariate and multivariate normality for the 64

observed variables (items) was rejected (skewness and kurtosis: χ2=2235.43 p<0.05).

Robust Maximum Likelihood Estimation was employed to derive model parameter

estimates. The validity extension results for the four measurement models are presented in

table 20, whilst table 21 contains the factor loadings obtained for model M2a. The internal

reliability results closely resemble the Australian and New Zealand results.

USA (n=287)









Total EI (M2)* 232.42 24.52 0.75 7

Total EI (M2a)** 187.54 20.41 0.76 5


df


(close)

M1 4926.96* 4344.66* 1942 2.24 0.92 0.93 0.072 0.099 0.066

(0.063; 0.068)

0.00

M2 4447.03* 3936.07* 1931 2.04 0.94 0.94 0.066 0.090 0.060

(0.058; 0.063)

0.00

M2a 2850.58* 2391.10* 1165 2.05 0.95 0.95 0.063 0.087 0.061

(0.057; 0.064)

0.00

M3 3527.47* 3134.90* 1916 1.64 0.96 0.96 0.051 0.070 0.047

(0.044; 0.050)

0.94

Table 19

Subscale internal reliabilities, means and standard deviations for USA sample (n=287) models M2 and M2a




* p < 0.001

Table 20

Goodness-of-fit statistics results of the single-group SUEIT CFAs for USA sample (n=287).



116


7 0.73

10 0.36

14 0.65

20 0.68

26 0.36

32 0.66

37 0.23

42 0.65

61 0.46

1 0.63

5 0.66

8 0.45

13 0.51

16 0.64

17 0.70

22 0.69

23 0.34

27 0.65

29 0.46

33 0.54

34 0.74

38 0.58

43 0.49

45 0.66

48 0.72

52 0.55

56 0.61

59 0.68

63 0.49

2 0.61

11 0.66

49 0.78

54 0.81

57 0.75

60 0.70

64 0.46

15 0.55

28 0.51

39 0.70

44 0.59

58 0.34

4 0.59

6 0.55

9 0.72

19 0.56

25 0.51

31 0.73

36 0.47

41 0.68

46 0.57

Table 21

Summary of RML completely standardised parameter estimates for model M2a for USA


117

Close fit evidence was only achieved for model M3 – similar to the Australian results. As

expected, goodness of fit systematically increased from model M1 to M3. Here, model M2

obtained NNFI and CFI values which approach the 0.95 cutoff, whilst clear evidence for

good model fit was indicated by the other results (RMSEA = 0.06, SRMR = 0.09). M3 once

again obtained the best fit. All factor loadings were statistically significant and ranged

from 0.23 (item 37) to 0.81 (item 54).

4.5.1.4 Italian results

Appendix 3 contains the descriptive statistics for M1 and M3, whilst table 22 contains the

descriptive statistics for measurement models M2 and M2a in this sample. The univariate

and multivariate normality of the 64 observed variables was inspected with PRELIS 2.8

(Jöreskog & Sörbom, 2002). The null hypothesis of multivariate normality (skewness and

kurtosis: χ2=4370.41, p=0.05) was rejected and hence the Satorra-Bentler chi-square

statistic is reported. Apart from the two subscales with the least number of items where

internal consistency is far below the acceptable benchmark of 0.70 (Nunnaly & Bernstein,

1994) the Emotional Management Self (and others) subscales also obtained an

unacceptably low alpha values. More specifically, the results revealed that the item 2 (“I

generate positive moods and emotions within myself to get over being frustrated at work”),

item 64 (“When a colleague upsets me at work, I think through what the person has said

and find a solution to the problem”) and item 54 (“I can easily snap out of feeling down at

work”) obtained negligible corrected item total correlations with the other items in the scale

(item 2 = 0.01, item 54 = 0.12 and item 64 = -0.01). This suggests that these items are not

measuring the same underlying construct24

.

24

Normally the standard procedure would be to remove these items from the scale before continuing with

other analyses. In this case the items were retained for the CFA analyses, so as to ascertain the practical

implications thereof, should the SUEIT be used in Italy in this form.

118

Italy (n=320)









Total EI (M2)* 211.14 19.98 0.57 7

Total EI (M2a)** 170.39 18.14 0.62 5


df


(close)

M1 5151.72* 4213.28* 1942 2.17 0.79 0.80 0.10 0.096 0.061

(0.058; 0.063)

0.00

M2 4918.35* 4015.90* 1931 2.08 0.81 0.82 0.099 0.092 0.058

(0.056; 0.061)

0.00

M2a 3026.44* 2421.26* 1165 2.08 0.84 0.85 0.099 0.093 0.058

(0.055; 0.061)

0.00

M3 3963.59* 3221.02* 1916 1.68 0.88 0.89 0.088 0.082 0.046

(0.043; 0.049)

0.99

Table 23 provides the goodness-of-fit results for the single group CFA analyses over the

four measurement models. Even though the obtained RMSEA values over the four models

closely resemble the values obtained in the Australian analyses (which range from 0.060 to

0.041), poor incremental fit values emerged over all the models. Even when close fit was

achieved with model M3, the NNFI and CFI values did not meet the 0.90 cutoff criteria.

Table 24 contains the factor loadings for model M2a. Three items (items 64, 2, 32) obtain

non significant loadings smaller than |0.15|. Item 58 (“When colleagues get worked up I

stay out of their way”) obtained a non-significant loading (0.16). Item 37 (“Colleagues

Table 22

Subscale internal reliabilities, means and standard deviations for Italian sample (n=320) models M2 and M2a.




* p < 0.001



Table 23

Goodness-of-fit statistics results of the single-group SUEIT CFAs for Italian sample (n=320).

119

know when I am worried”) obtained the highest loading (0.77). This is a direct contrast

with the Australian, New Zealand and USA results where this item consistently obtained

the lowest loading over the entire instrument.


7 0.28

10 0.63

14 0.40

20 0.31

26 0.43

32 0.05

37 0.77

42 0.36

61 0.74

1 0.52

5 0.53

8 0.19

13 0.43

16 0.45

17 0.49

22 0.39

23 0.28

27 0.40

29 0.39

33 0.26

34 0.63

38 0.48

43 0.48

45 0.51

48 0.60

52 0.28

56 0.54

59 0.42

63 0.45

2 -0.02

11 0.48

49 0.65

54 0.22

57 0.61

60 0.58

64 -0.04

15 0.41

28 0.41

39 0.31

44 0.44

58 0.16

4 0.49

6 0.51

9 0.47

19 0.43

25 0.62

31 0.69

36 0.30

41 0.24

46 0.47

Table 24

Summary of RML completely standardised parameter estimates for model M2a for Italy

NOTE: All factor loadings are statistically significant (p<0.05) unless underlined.

120

4.5.1.5 Sri Lanka results

The means, standard deviations, and Cronbach Alpha’s per subscale for models M2 and

M2a, are presented in table 25 (see appendix 3 for the descriptive statistics of M1 and M3).

Consistent with the previous results, low alpha values were obtained for the two scales with

the least number of items (Emotional Recognition and Emotional Management Others)25

.

Contrary to the Italian results, and in line with the Australian, New Zealand and USA

results, the Emotional Management Self subscale obtained a relatively high alpha value

(0.74). This indicates that all the items contribute to an internally consistent description of

the Emotional Management Self dimension in this sample. Investigation of the univariate

and multivariate normality of the 64 observed variables revealed the rejection of the null

hypothesis of multivariate normality (skewness and kurtosis: χ2=8119.061, p=<0.05).

Results of the CFA analyses for models M2a and M3, with Robust Maximum Likelihood

estimation, is reported in table 26. No results for M1 and M2 are reported. For these two

measurement models, estimation problems were encountered and the solutions failed to

converge.

Sri Lanka (n=587)









Total EI (M2)* 219.41 17.47 0.59 7

Total EI (M2a)** 178.80 17.39 0.73 5

25

Normally the standard procedure would be to remove these items from the scale before continuing with

further analyses. Similar to the Italian results these items were retained for the CFA analyses, so as to

ascertain the practical implications thereof, should the SUEIT be used in Sri Lanka in this form.

Table 25

Subscale internal reliabilities, means and standard deviations for Sri Lanka sample (n=587) models M2 and M2a.



121

The absolute fit indices for Model M2a indicated reasonable to acceptable model fit, as an

RMSEA of 0.066 and SRMR of 0.080 were obtained. However, the incremental fit indices

did not meet the 0.90 cutoff. For M3 close fit was achieved as the H0: RMSEA ≤ 0.05

could not be rejected given that a p-value for the test of close fit (RMSEA < 0.05) equal to

0.17 emerged. Here the SRMR achieved the <0.08 cutoff, indicating a well-fitting model.

However, although the RMSEA value fell below the 0.06 cutoff for well-fitting models, the

incremental fit indices did not lean towards 0.95 as one would expect for a model that has

achieved close fit (e.g. as with the Australian, New Zealand results).


df


(close)

M2a 5297.12* 4149.78* 1165 3.56 0.87 0.87 0.061 0.080 0.066

(0.064; 0.068)

0.00

M3 5974.71* 4862.49* 1916 2.54 0.91 0.92 0.054 0.073 0.051

(0.049; 0.053)

0.17

As is evident in table 27, all the factor loadings were statistically significant and ranged

from 0.21 (item 37) to 0.70 (item 31: “I find it difficult to think clearly when I’m feeling

anxious about something at work”). In line with the Australian, New Zealand and USA

results, item 37 obtained the lowest loading over the whole instrument.




* p < 0.001

Table 26

Goodness-of-fit statistics results of the single-group SUEIT CFAs for Sri Lanka sample (n=587).

122


7 0.58

10 0.26

14 0.61

20 0.54

26 0.33

32 0.45

37 0.21

42 0.57

61 0.35

1 0.48

5 0.51

8 0.49

13 0.45

16 0.23

17 0.48

22 0.52

23 0.47

27 0.45

29 0.50

33 0.50

34 0.41

38 0.43

43 0.50

45 0.51

48 0.38

52 0.52

56 0.46

59 0.58

63 0.39

2 0.35

11 0.60

49 0.67

54 0.45

57 0.65

60 0.65

64 0.40

15 0.36

28 0.27

39 0.56

44 0.42

58 0.23

4 0.39

6 0.44

9 0.59

19 0.43

25 0.55

31 0.70

36 0.28

41 0.42

46 0.48

4.5.1.6 South African White results

The descriptive statistics for measurement models M2 and M2a for this sample is presented

in table 28 (refer to appendix 3 for M1 and M3 descriptive statistics). The null hypotheses

Table 27

Summary of RML completely standardised parameter estimates for model M2a for Sri Lanka


123

of univariate and multivariate normality was rejected (multivariate skewness and kurtosis:

χ2=3309.04 p<0.05). Robust Maximum Likelihood Estimation was employed to derive the

model parameter estimates for the four measurement models, presented in table 29. All the

subscales (except for Emotional Recognition) obtained alpha values well above the 0.70

cutoff. The alpha values ranged from 0.76 (Emotional Control and Emotional Expression)

to 0.89 (Understanding Emotions). The α=0.33 obtained for the Emotional Expression

subscale is in line with the Italian and Sri Lanka results and may be subscribed to the fact

that the scale only contains two items (“At work, I can detect my emotions as I experience

them” and “I find it hard to distinguish my emotions at work”).

South African White sample (n=290)









Total EI (M2)* 216.33 20.21 0.59 7

Total EI (M2a)** 175.67 19.25 0.73 5


df


(close)

M1 8745.42* 6892.84* 1942 3.55 0.80 0.81 0.11 0.14 0.094

(0.092; 0.096)

0.00

M2 7761.03* 6080.73* 1931 3.15 0.84 0.84 0.10 0.13 0.086

(0.084; 0.089)

0.00

M2a 6072.63* 4627.44* 1165 3.97 0.80 0.81 0.10 0.13 0.10

(0.098; 0.10)

0.00

M3 6759.26* 5295.31* 1916 2.76 0.86 0.87 0.084 0.11 0.078

(0.076; 0.081)

0.00




* p < 0.001

Table 29

South African White (n=290) goodness-of-fit statistics results for the single-group SUEIT CFAs

Table 28

Subscale internal reliabilities, means and standard deviations for South African White sample (n=290) models M2 and

M2a.



124


7 0.37

10 0.77

14 0.26

20 0.29

26 0.63

32 0.19

37 0.66

42 0.34

61 0.80

1 0.57

5 0.59

8 0.67

13 0.55

16 0.48

17 0.56

22 0.50

23 0.40

27 0.58

29 0.48

33 0.47

34 0.66

38 0.48

43 0.51

45 0.55

48 0.64

52 0.54

56 0.57

59 0.60

63 0.47

2 0.29

11 0.71

49 0.78

54 0.44

57 0.75

60 0.69

64 0.26

15 0.58

28 0.61

39 0.44

44 0.61

58 0.19

4 0.36

6 0.53

9 0.69

19 0.24

25 0.68

31 0.76

36 0.23

41 0.35

46 0.62

Consistent with the previous results, the model fit improves as the number of underlying

latent variables increase. However, poor model fit was obtained over all the measurement

models. The >0.10 SRMR, and very poor fitting incremental fit index values (NNFI range

Table 30

Summary of RML completely standardized parameter estimates for model M2a for the SA White sample


125

from 0.80 – 0.86, and CFI range from 0.81 – 0.87) does not meet the cutoff criteria for

reasonable / acceptable fit. The RMSEA results support this, as the best value obtained

(M3) only marginally fell below the 0.080 cutoff for acceptable fit.

The factor loadings were all statistically significant and ranged from 0.19 (for item 32, “At

work I have trouble finding the right words to express how I feel” and item 58, “When

colleagues get worked-up I stay out of their way”) to 0.80 (item 61, “Colleagues can easily

tell how I feel”). In contrast with the Australian, and USA results where only one item

loading below 0.30 was obtained, in this sample 8 items obtained loadings lower than 0.30.

4.5.1.7 South African non-White results

Table 31 contains the means, standard deviations, and Cronbach Alpha’s per subscale for

models M2 and M2a (see appendix 3 for M1 and M3 descriptive statistics). Consistent with

the previous results, Emotional Recognition obtained the lowest alpha value. The value

obtained here is, however, the lowest over all the samples and would cast serious doubt on

using this subscale in any further analyses. Two other subscales obtained values below the

0.70 cutoff (i.e. the Emotional Management Others subscale with a value of 0.47 and the

Emotions Direct Cognition subscale which obtained a value of 0.65). In comparison with

the Emotional Management Others subscale which only has five items, the Emotions Direct

Cognition scale is the second longest subscale in the instrument (12 items). The relative

low alpha here, casts doubt on the internal consistency of the items contained in this scale,

when used in this sample. Both the null hypotheses of univariate and multivariate normality

for the 64 observed variables (items) were evaluated with PRELIS 2.8 (Jöreskog &

Sörbom, 2002) and was rejected (skewness and kurtosis: χ2=6080.94, p<0.05). Robust

Maximum Likelihood Estimation was employed to derive the model parameter estimates

presented in table 32. Similar to the Sri Lanka results, no results for models M1 and M2 are

reported. Estimation problems were encountered and the solutions failed to converge.

126

South African Non-White sample (n=337)









Total EI (M2)* 211.12 25.29 0.69 7

Total EI (M2a)** 170.76 23.89 0.79 5


df


(close)

M2a 5503.49* 4043.73* 1165 3.47 0.87 0.88 0.13 0.12 0.086

(0.083; 0.089)

0.00

M3 5412.10* 4191.58* 1916 2.19 0.91 0.92 0.10 0.090 0.059

(0.057; 0.062)

0.00

From the results it is clear that for model M2a poor fit was achieved (RMSEA <0.09 and

SRMR >0.10). The poor incremental fit index values (NNFI=0.87 and CFI = 0.88) further

confirm the poor fit for this measurement model. A notable improvement is evident for

model M3. Here a RMSEA <0.060 indicative of a well-fitting model, coupled with NNFI

and CFI values that obtained the 0.90 cutoff provide evidence for a well-fitting model. Two

items obtained non significant loadings (items 58 and 64) smaller than |0.15|. However, no

other item loadings under 0.30 were obtained. Item 57 (“I find it difficult to maintain

positive moods and emotions when I’m under stress”) obtained the highest loading (0.82).




* p < 0.001

Table 32

South African Non-White (n=337) goodness-of-fit statistics results of the single-group SUEIT CFAs

Table 31

Subscale internal reliabilities, means and standard deviations for South African Non-White sample (n=337) models

M2 and M2a.



127


7 0.43

10 0.52

14 0.31

20 0.55

26 0.53

32 0.41

37 0.63

42 0.35

61 0.61

1 0.54

5 0.51

8 0.43

13 0.60

16 0.46

17 0.66

22 0.43

23 0.51

27 0.43

29 0.52

33 0.49

34 0.58

38 0.45

43 0.64

45 0.50

48 0.54

52 0.46

56 0.47

59 0.63

63 0.47

2 0.33

11 0.69

49 0.69

54 0.39

57 0.82

60 0.77

64 0.10

15 0.48

28 0.49

39 0.45

44 0.48

58 0.03

4 0.37

6 0.63

9 0.67

19 0.35

25 0.65

31 0.73

36 0.30

41 0.40

46 0.67

Table 33

Summary of RML completely standardised parameter estimates for model M2a for SA Non-White sample


128

4.5.2 Results: Validity generalisation

Models M1-M3 are nested models. In Model M2a two subscales (Emotional Recognition

and Emotions Direct Cognition) were omitted from the seven factor structure measurement

model (M2). Hence, the chi-square difference test26

could not be used to compare M1- M3

with M2a. An alternative strategy, the comparison of the Expected Cross-Validation Index

(ECVI; Brown & Cudeck, 1989) and Akaike’s Information Criterion (AIC)27

, was used.

The ECVI is most useful in comparing the performance of one model to another (Hair et

al., 2006). Table 34 lists the values obtained for the various samples and models. Lower

values indicate better fit as they are a function of the minimum values of the population

discrepancy function (Mels, personal communication, 2008).

From the results it is clear that model M2a, consistently obtained the lowest ECVI and AIC

values over all the samples. The only exception is with the Australian sample A where the

ECVI = 4.63, and AIC = 7415.53 for model M3 was lower than for model M2a (ECVI =

4.93, AIC = 7896.40). Based on these results, model M2a was used as the baseline model

for the invariance analyses.

4.5.3 Discussion: validity extension and generalisation results

Validity extension is a form of model cross-validation (Diamantopoulos & Siguaw, 2000).

The aim of applying this loose replication strategy (Bentler, 1980) was to assess the

factorial validity of various SUEIT measurement models over various cultural groups. It is

proposed that the successful replication of a given measurement model (i.e. M1 / M2 / M2a

/ M3) in different cultures may provide some support for regarding EI (and its various

facets as encapsulated by the SUEIT) as a universal individual difference (at least over the

limited cultural groups included in this study). The practice of fitting competing factor

26

The chi-square difference test can only be used for comparison of nested models. According to (Mels,

personal communication, 2008) models are considered to be nested when the parameter estimates are derived

from exactly the same items and covariation matrix. With the omission of 14 items for the purposes of fitting

model M2a, this assumption is violated, and an alternative strategy was used to compare the four

measurement model results. 27

The information criteria require a sample size of at least 200 to make its application reliable. This fit

measure is also affected by departures from multivariate normality (Diamantopoulos & Sigauw, 2000). Both

these conditions were met in the present study as all samples had more than 200 cases, and multivariate

normality problems were addressed by employing RML estimation.

M1 M2 M2a M3

Model ECVI

(Indep, Sat)

Model AIC

(Indep, Sat)

Model ECVI

(Indep, Sat)

Model AIC

(Indep, Sat)

Model ECVI

(Indep, Sat)

Model AIC

(Indep, Sat)

Model ECVI

(Indep, Sat)

Model AIC

(Indep, Sat)

Australia A (n=1604) 8.47

(99.40; 2.60)

13574.53

(159336.79; 4160.00)

7.10

(96.75; 2.60)

11376.90

(155086.08; 4160.00)

4.93

(72.42; 1.59)

7896.40

(116085.02; 2550.00)

4.63

(99.40; 2.60)

7415.53

(159336.79; 4160.00)

Australia B (n=1605) 8.20

(94.53; 2.59)

13156.06

(151626.75; 4160.00)

6.82

(91.83, 2.59)

10947.12

(147289.10; 4160.00)

4.39

(68.51; 1.59)

7043

(109885.57; 2550.00)

4.62

(94.53; 2.59)

7407.35

(151626.75; 4160.00)

USA (n=287) 16.16

(122.33; 14.55

4620.66

(34986.49; 4160.00)

14.80

(122.33; 14.55)

4234.07

(34986.49; 4160.00)

9.13

(91.27; 8.92)

2611.10

(26102.19; 2550.00)

12.11

(122.33; 14.55)

3462.90

(34986.49; 4160.00)

New Zealand (n=234) 16.40

(84.05; 17.85)

3821.37

(19582.57; 4160.00)

15.08

(84.05; 17.85)

3512.64

(19582.57; 4160.00)

9.21

(58.62; 10.94)

2145.82

(13657.56; 2550.00)

12.43

(84.05;17.85)

2895.67

(19582.57; 4160.00)

Italy (n=320) 14.07

(42.46; 13.04)

4489.28

(13545.29; 4160.00)

13.52

(42.46; 13.04)

4313.90

(13545.29; 4160.00)

8.28

(29.82, 7.99)

2641.26

(9511.33; 2550.00)

11.13

(42.46; 13.04)

3549.02

(13545.29; 4160.00)

SA White (n=290) 24.81

(98.50; 14.39)

7168.84

(28466.83; 4160.00)

22.07

(98.50; 14.39)

6378.73

(28466.83; 4160.00)

16.77

(68.94; 8.82)

4847.44

(19924.99; 2550.00)

19.46

(98.50; 14.39)

5623.31

(28466.83; 4160.00)

SA Non-White (n=337) - - - - 12.65

(72.37; 7.57)

4263.73

(24388.72; 2550.00)

13.41

(88.56; 12.34)

4519.58

(29843.04; 4160.00)

Sri Lanka (n=587) - - - - 7.46

(42.61; 4.35)

4369.78

(24968.93; 2550.00)

8.78

(62.38; 7.04)

5190.49

(36866.86; 4160.00)

Table 34

Values for absolute fit indices over all countries for the four measurement models

NOTE: M1 = original five factor structure (Palmer & Stough, 2001); M2 = seven factor structure (Stough, personal communication, 2007; M2a = modified five factor structure; M3 =

nine factor structure (Gignac, 2005); Model ECVI / AIC = values derived for the model at hand; Indep = ECVI / AIC for Independence model (a model in which all observed variables

are uncorrelated, Diamantopoulos & Siguaw, 2000); Sat = ECVI / AIC for Saturated model (a model in which the number of parameters to be estimated is exactly equal to the number

of variances and covariances among the observed variables; Diamantopoulos & Siguaw, 2000),

130

structures over different cultures (as was done here), may strenghen this finding as it allows

for a wider investigation regarding possible construct bias over different cultural groups.

That is, by conducting a validity generalisation investigation more / different facets of the

construct were assessed over different cultural groups. However, single group CFA analysis

is a weaker test of investigating construct bias than when configural invariance is tested

over different groups (i.e. analyses presented in section 4.5.4).

From the results it is clear that the original five factor model (Palmer & Stough, 2001) is

the least adequate representation of the data in all the samples. For Australia, the USA and

New Zealand, both the absolute fit index results (SRMR and RMSEA) obtained values that

fell just within the range for well fitting models (SRMR from 0.1 to 0.08; RMSEA from

0.059 to 0.066). In addition, the incremental fit indices confirmed this with values ranging

from 0.90 to 0.93. Generally, it would seem that as the CD between Australia and the non-

Western countries increases, the fit of the five factor model deteriorated substantially. For

example, the five factor model was non admissible in the Sri Lanka and South African non-

White data. For the South African White data very poor model fit was achieved (RMSEA =

0.094, NNFI = 0.80, CFI=0.81). Gignac (2005) conducted a series of unrestricted factor

analyses on the 64 items of the SUEIT (with an Australian sample) and reported the

existence of 6 substantive factors (i.e. Emotional Management Self, Understanding

Emotions External, Emotional Expression, Emotions to Direct Cognition, Emotional

Control, and Emotional Management of Others) that explained 37.2% of the variance,

collectively. In addition, he also concluded that there was evidence suggesting that the

factor structure contained item keyed method variance within the Australian data. In the

light of this finding, it could be argued that the poor fitting five factor structure may be due

to the fact that more factors are needed to represent the data, and/or that method bias is

leading to a distortion of the CFA results (an hypothesis that is investigated in section 4.5.6

of this dissertation). On a practical level, however, these results suggest that the five factor

structure should not be considered generalisable over the cultural groups included in this

study.

131

A fairly clear pattern emerged for the rest of the results. Consistent with known SEM

convention, as the number of latent factors that was modeled increased, so did the

goodness-of-fit results. More specifically, close fit was achieved for the nine factor model

in all, but for the two South African, samples. In the South African White sample, the nine

factor model obtained better fit than models M2 and M2a, although the results here would

still be considered poor (RMSEA = 0.078, SRMR=0.11, NNFI=0.86, CFI=0.87) by

conventional standards. Hence, it is concluded that the nine factor model (M3) was the

model with the best generalisability potential over all the samples. The model with the

second best generalisation potential is model M2a. This model, generally, obtained better

fit indices than model M2 (with the exception of the Australia A and South African White

data). No admissible solutions could be derived for model M2 in the South African non-

White and Sri Lanka data. However, with the omission on the Emotional Recognition and

Emotions Direct Cognition subscales, admissible solutions for model M2a in these two

groups were obtained, although the fit was poor (SA Non-White: RMSEA=0.086,

SRMR=0.12, NNFI=0.87, CFI=0.88; Sri Lanka: RMSEA=0.066, SRMR = 0.080, NNFI =

0.87; CFI = 0.87).

The main goal for investigating the validity generalisation potential of different

measurement models was to identify the model that would replicate the best over all the

samples. This model was used as the baseline model for the invariance analyses. Although

the nine factor model (M3) showed the best generalisation potential, four reasons are put

forward why model M2a was used as the baseline model for the invariance analyses. First,

the nine factor model is very complex (64 items, 9 factors). Various attempts to execute

multi-group CFA with this model failed. Model M2a is less complex with 50 observed

variables and 5 latent factors and MI results were attainable over all the samples. Secondly,

after splitting the original Emotional Recognition and Expression subscale into two

subscales (i.e. Emotional Recognition and Emotional Expression) only two items were

retained in the Emotional Recognition subscale. According to Hair et al., (2006) constructs

with only one or two items increase the likelihood of problems with interpretational

confounding, as well as increasing the higher likelihood of estimation problems. Others

have suggested that a minimum of 4 items per construct is required for SEM research

132

(Muliak & Millsap, 2000). Thirdly, according to Gignac (2005) the negatively and

positively keyed items in the Emotions Direct Cognition subscale share respective

substantive and item keyed variance, making this scale very complicated28

. Gignac (2005)

reported, for example, the largest difference (d=1.96) between the two conceptually

equivalent, but oppositely keyed items, “Examination of feelings is useful in solving work

related problems” and “My problem solving at work is based on sound reasoning rather

than feelings”. He conclude that (Gignac, 2005, p.155), “it would appear that the phrasing

of the positively keyed items makes it easier to endorse. Individuals might believe that the

use of emotions in this context can be useful, however, not to the extent of not using

reasoning. Further, the negatively keyed item has a possible connotation that problem

solving should be based on feelings and not reasoning, which is not what the theory

underlying EDC is (or should be) about”. Lastly, the subscales that remained in model

M2a, after omission of Emotions Direct Cognition and Emotional Recognition, are the core

EI facets that pertain to emotional expression and regulation (i.e. management and control).

Most of the theoretical arguments put forward in chapter three focused on these two aspects

of EI. No substantive arguments regarding proposed cross-cultural differences in emotional

reasoning (i.e. use of emotions in thought), was proposed. Hence, the Emotions Direct

Cognition subscale was not deemed a core area of investigation in this study. In the light of

these considerations, model M2a was utilised as the best fitting baseline model for the

purposes of the invariance analyses.

4.5.4 Results: cross-cultural validation (testing MI)

The MI analyses (series of multi-group analyses) were carried out with measurement model

M2a, utilising the item raw scores in the all the samples. In all the analyses, the fully

unconstrained model (configural invariance model, M0) was used as the fixed baseline

model. The results of the configural invariance models were interpreted in terms of the

28

In a more recent analyses aimed at uncovering a taxonomic model of EI (Palmer, Gignac, Ekermans &

Stough, 2008), the Emotional Reasoning facet (i.e. the skill with which individuals’ reason with emotional

information in thought – the EDC scale in the SUEIT) did not emerge in a exploratory factor analysis of the

subscales of five prominent EI measures (MSCEIT, EQ-i; TMMS, TAS-20 and SEI). It would seem that this

facet of EI is currently not being reliably assessed by other existing measures, as seems to be case with the

EDC scale in the SUEIT.

133

different degrees of structural equivalence that was obtained over the different cultural

groups.

Next, the omnibus test for the equality of variance-covariance matrices (Vandenberg &

Lance, 2000), with the condition that all parameter estimates are set to be equal across

samples (M1), was conducted. If covariance matrices do not differ across groups, then

overall MI is establish, and further test of the other aspects of measurement equivalence are

not necessary. If, however, the null hypothesis (Σg = Σ

g’) of exact fit for model M1 (fully

constrained model) is rejected, then subsequent tests is warranted (Vandenberg & Lance,

2000). The critical question, however, is whether the model fit deteriorate significantly

when the equality constraint (fully constrained) was imposed on the model in comparison

to the multi-group analysis in which parameter estimates were allowed to differ across

samples (fully unconstrained). Hence, to establish the omnibus test result, the difference in

the Satorra-Bentler Chi-Square (based on adjustment formula; Satorra & Bentler, 1999)

under constrained and unconstrained conditions is calculated.

If full measurement invariance (omnibus test) was not achieved, then the test of metric

invariance was conducted next by constraining the factor loadings of like items on latent

variables, to be equal across groups (M2). Failure to reject the null hypothesis (Λg

x =

Λ

g x’)

would provide evidence for metric invariance over the different groups (Vandenberg &

Lance, 2000). Only if evidence of full metric invariance is found then the hypothesis that

the vector of item intercepts is invariant (scalar invariance, τg

x =

τ

g x’) across groups could

be conducted.

The MI results are presented in tables 35 – 40. Separate analyses29

were conducted for

every county with Australia as the reference group.

29

It is possible to conduct one invariance analysis with all the samples. However, the strategy to use separate

analyses served the goals of this study more accurately. That is, in order to establish a pattern of fit between

Australia and the other countries (and test whether it resembles any of the two proposed CDR patterns, see

hypotheses 1 and 2), the separate analyses needed to be executed. In addition, by conducting separate analyses

it was possible to match the age and gender composition of the Australian sample to every other county

sample, separately. If only one analysis was conducted, the comparability of the samples would have been

even lower, as none of them would have been matched on at least size, age and gender (as is currently the

case).

134

Invariance Model S-Bχ2 df S-Bχ2/

df

∆df ∆ S-Bχ2 NNFI

/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

3874.72* 2330 1.66 - - 0.94 0.94 0.053

(0.050;

0.056)

0.032 0.089

M1. Invariant variance-

covariance matrices

(Σg = Σg’)

1 versus 0

3963.10*

-

2440

-

1.62

-

-

110

-

107.971

0.94

-

0.94

-

0.052

(0.049;

0.055)

-

0.16

-

0.091

-


df


/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

4530.13* 2330 1.94 - - 0.95 0.95 0.057

(0.055;

0.060)

0.00 0.087


covariance matrices

(Σg = Σg’)

1 versus 0

4898.63*

-

2440

-

2.01

-

110

303.281

0.95

-

0.95

-

0.059

(0.057;

0.062)

-

0.00

-

0.12

-

M2. Invariance of factor

loadings (Λg = Λg’)

2 versus 0

4597.04*

-

2375

-

1.94

-

-

45

-

58.532

0.95

-

0.95

-

0.057

(0.055;

0.060)

0.00

-

0.094

-

Table 36

Results of the Australian (n=287) and USA (n=287) cross-national configural, omnibus and metric invariance analyses

Note: S-Bχ2, Satorra-Bentler Chi-square, NNFI / TLI; Non-Normed Fit Index / Tucker Lewis Index, CFI,

Comparative Fit Index; RMSEA, Root Mean Square Error of Approximation with 90% confidence interval;

SRMR, Standardised Root Mean Square Residual *p < 0.05; 1: p < 0.05 (p=0.00); 2: p > 0.05 (p=0.084)

Table 35

Results of the Australian (n=234) and New Zealand (n=234) cross-national configural and omnibus invariance analyses

NOTE: S-Bχ2, Satorra-Bentler Chi-square, NNFI / TLI; Non-Normed Fit Index / Tucker Lewis Index, CFI,


SRMR, Standardised Root Mean Square Residual *p<0.05; 1: p > 0.05 (p=0.536)

135


df


/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

4566.58* 2330 1.96 - - 0.92 0.93 0.055

(0.052;

0.057)

0.00 0.093


covariance matrices

(Σg = Σg’)

1 versus 0

7157.78*

-

2440

-

2.93

-

-

110

-

742.98*

0.85

-

0.85

-

0.078

(0.076;

0.080)

-

0.00

-

0.14

-



2 versus 0

5094.99*

-

2375

-

2.14

-

-

45

-

441.01*

0.91

-

0.91

-

0.060

(0.058;

0.062)

0.00

-

0.094

-


df


/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

7213.10* 2330 3.09 - - 0.87 0.88 0.085

(0.083;

0.087)

0.00 0.13


covariance matrices

(Σg = Σg’)

1 versus 0

9273.17*

-

2440

-

3.80

-

-

110

-

1123.95*

0.83

-

0.83

-

0.098

(0.096;

0.10)

-

0.00

-

0.15

-



2 versus 0

7915.00*

-

2375

-

3.33

-

-

45

-

210.10*

0.86

-

0.86

-

0.090

(0.088;

0.092)

0.00

-

0.13

-

Table 38

Results of the Australian (n=290) and South African White (n=290) cross-national configural, omnibus and metric

invariance analyses



SRMR, Standardized Root Mean Square Residual *p < 0.05

Table 37

Results of the Australian (n=320) and Italian (n=320) cross-national configural, omnibus and metric invariance analyses



SRMR, Standardised Root Mean Square Residual *p < 0.05

136


df


/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

6197.86* 2330 2.66 - - 0.90 0.91 0.070

(0.068;

0.072)

0.00 0.11


covariance matrices

(Σg = Σg’)

1 versus 0

9068.13*

-

2440

-

3.72

-

-

110

-

880.65*

0.84

-

0.85

-

0.090

(0.088;

0.092)

-

0.00

-

0.17

-



2 versus 0

6770.43*

-

2375

-

2.85

-

-

45

-

470.58*

0.89

-

0.90

-

0.074

(0.072;

0.076)

0.00

-

0.11

-


df


/ TLI

CFI RMSEA

(CI)

P

(close)

SRMR

M0. Unconstrained

(number of factors

invariant)

7770.82* 2330 3.34 - - 0.91 0.91 0.063

(0.062;

0.065)

0.00 0.080


covariance matrices

(Σg = Σg’)

1 versus 0

9366.94*

-

2440

-

3.83

-

-

110

-

938.91*

0.89

-

0.89

-

0.070

(0.068;

0.071)

-

0.00

-

0.10

-



2 versus 0

8029.65*

-

2375

-

3.38

-

-

45

-

258.69*

0.91

-

0.91

-

0.064

(0.062;

0.065)

0.00

-

0.085

-

Table 39

Results of the Australian (n=337) and South Africa Non-White (n=337) cross-national configural, omnibus and metric

invariance analyses

Table 40

Results of the Australian (n=587) and Sri Lanka (n=587) cross-national configural, omnibus and metric invariance

analyses



SRMR, Standardised Root Mean Square Residual *p < 0.05



SRMR, Standardized Root Mean Square Residual *p < 0.05

137

4.5.5 Discussion: cross-cultural validation

Construct bias

This discussion will focus on all the results, as well as the specific hypotheses that were

investigated. These included:

Hypothesis 1(a): The construct bias pattern of influence on the transportability of the

SUEIT will resemble CDR pattern 1 (described in tables 4 & 6).

Hypothesis 1(b): The construct bias pattern of influence on the transportability of the

SUEIT will resemble CDR pattern 2 (described in table 5).

In the Australian, New Zealand analyses the null hypothesis of Σg = Σ

g’ could not be

rejected (∆ S-Bχ2

(110) = 107.97, p>0.05). Thus, evidence for full measurement invariance

was attained in this sample. No further tests of invariance were needed here (Vandenberg &

Lance, 2000) and latent mean scores may be compared directly over these two cultural

groups. Hence, the results suggest that the instrument may be transported and used in New

Zealand to make meaningful inferences over these two groups.

For the separate Australian and USA, Italian, South African White and non-White, as well

as Sri Lanka analyses the results of the omnibus test revealed that full invariance was not

achieved (USA: ∆ S-Bχ2

(110) = 303.28, p<0.05; Italian: ∆ S-Bχ2

(110) = 742.98, p<0.05; South

African White: ∆ S-Bχ2

(110) = 1123.95, p<0.05; South African Non-White: ∆ S-Bχ2

(110) =

880.65, p<0.05; Sri Lanka: ∆ S-Bχ2

(110) = 938.91, p<0.05).

As expected (due to the omnibus test results), the best configural invariance (M0) results

were attained in the Australian, New Zealand analyses. Here the p-value for close fit

(p=0.032) leaned towards the p>0.05 cutoff and the incremental fit indices leaned towards

0.95 (NNFI = 0.94, CFI=0.94). The RMSEA of 0.053 underscored this result. Although the

Australian, USA analysis attained slightly better NNFI and CFI values for model M0 (0.95),

the rest of the results suggested that the configural invariance fit was not as good as for the

New Zealand analyses (i.e. RMSEA = 0.057, p(close fit)<0.05). The results, however, still

indicate a very well fitting model (RMSEA < 0.060, CFI and NNFI = 0.95, SRMR<0.09).

The next best configural invariance model fit was achieved with the Australian, Italian

138

analysis. Here the RMSEA of 0.055 is slightly better than the value obtained in the USA

analyses. However, in comparison with the other USA results, less good fit was indicated

(NNFI=0.92, CFI=0.93, SRMR=0.093). Surprisingly, the results suggested that the

Australian, Sri Lanka M0 model fit was better than for both the South African analyses.

More specifically, here a RMSEA=0.063 and NNFI=0.90, CFI=0.91 was achieved. The

SRMR also attained the 0.080 cutoff. The Australian, South African non-White data results

(RMSEA=0.070, NNFI=0.90, CFI=0.91 and SRMR=0.11) was better than the Australian,

South African White results (RMSEA=0.085, NNFI=0.87, CFI=0.88, SRMR=0.13). Hence

the pattern for the configural invariance results is as follows: New Zealand, USA, Italy, Sri

Lanka, South Africa non-White and South Africa White. This sequence does not

correspond with either of the proposed CDR patterns (pattern 1 or 2). Thus, neither

hypothesis 1(a) or 1(b) is supported.

The results do, however, provide some support for the transportability of the instrument, at

least at structural equivalence level30

, to all the samples (although the South African White

results were mediocre). The New Zealand and USA results also provide some evidence to

suggest that for the smallest cultural distance (i.e. all Western countries) the influence of

construct bias would seem to be the least. That is, the psychometric properties of the

instrument would seem to not be adversely impacted when the instrument is transported to

these cultures.

As evidence for configural invariance was established in all the samples, the metric

invariance test was conducted next. The results revealed that full metric invariance was

obtained in the Australia, USA analysis (∆ S-Bχ2

(45) = 58.53, p>0.05). The pattern of non -

metric invariance, according to the chi-square different test, is as follows: South Africa

White (∆ S-Bχ2

(45) = 210.10, p<0.05), Sri Lanka (∆ S-Bχ2

(45) = 258.69, p<0.05), Italy (∆ S-

Bχ2

(45) = 441.01, p<0.05), South African non-White (∆ S-Bχ2

(45) = 470.58, p<0.05). Model

comparisons may also be evaluated based on the change in the CFI. More specifically,

changes in CFI of -0.01 or less indicate that the invariance hypothesis should not be

30

This is a replication / confirmation of the Tucker’s Phi results (see appendix 4) where congruence

coefficients of >0.98 was achieved over all the samples (except for the Italian Emotional Management self

subscale).

139

rejected. Possible differences may be evidenced by a change of between -0.01 and -0.02,

whilst changes greater than -0.02 point to a definite lack of invariance (Vandenberg &

Lance, 2000). In the Italian and South African White results CFI changes of -0.02 was

obtained, supporting the lack of metric invariance. In the Sri Lanka results, no change in the

CFI was evident, whilst a -0.01 change was observed for the South African non-White

analysis. This may suggest that for these two samples, according to the practical

significance test, full metric invariance was obtained. The remaining fit indices in the Sri

Lanka results indicated good model fit (RMSEA=0.064, SRMR=0.085) whilst the same

was not true for the South African non-White results (RMSEA=0.074, SRMR=0.11). This

does cast some doubt on the plausibility of assuming full metric invariance did hold here.

After consideration of all the results obtained over all the samples, the overall pattern for

(non)metric invariance may be listed as follows: USA (∆ S-Bχ2

(45) = 58.53, p>0.05, ∆CFI

=0), Sri Lanka (∆ S-Bχ2

(45) = 258.69, p<0.05, ∆CFI = 0), South Africa Non-White (∆ S-Bχ2

(45) = 470.58, p<0.05, ∆CFI= -0.01), Italy (∆ S-Bχ2

(45) = 441.01, p<0.05, ∆CFI=-0.02) and

South Africa White (∆ S-Bχ2

(45) = 210.10, p<0.05, ∆CFI=-0.02). It was proposed that lack

of metric invariance is likely due to method bias (Van Herk et al., 2004). Thus, it could be

argued that the possibility of a common method artifact may have obscured the South

African, and possibly the other results, influencing the viability of the MI testing

procedures. Hence, the method bias results are reported next.

4.5.6 Results: method bias

Two sources of method bias (i.e. cultural response styles attributed to national cultural

dimension differences and the influence of verbal ability / bilingualism of respondents) on

the MI results were investigated. Two approaches were followed.

Response styles: ERS and ARS indices

In this study national differences in response styles are viewed as a potential source of

method bias and, thus, as potentially resulting in a distortion of cross-cultural differences in

the target variables. The metric invariance results were further investigated by exploring the

ERS and ARS per sample and subscale. In order to obtain a comprehensive overview the

ERS and ARS indices were calculated for all the samples.

140

Item content n Items Australia USA New-Zealand SA White SA Non -White Italy Sri Lanka F η2

Emotional Expression 9 0.442 a, b, c, d 0.417 a, b, c, d 0.470 a, b, c, d 0.217 b, e 0.051 e 0.141 e 0.317 73.594 0.09

Understanding Emotions External 20 0.749 a, b, c, d 0.728 a, b, c, d 0.769 a, b, c, d 0.559 b, c 0.414 e, f, 0.483 0.567 127.614 0.16

Emotional Management Self 7 0.569 a, b, c, d 0.526 a, b, c 0.609 a, b, c, d 0.392 c 0.358 c, e 0.219 e 0.448 47.385 0.06

Emotional Management Others 5 0.398 a, b, c 0.408 a, b, c 0.403 a, b, c 0.237 c, e 0.217 c, e 0.093 e 0.393 40.646 0.06

Emotional Control 9 0.558 a, b, c, d 0.523 b, c, d 0.608 a, b, c, d 0.443 c 0.383 c 0.289 e 0.416 40.511 0.06

Item content n Items Australia USA New-Zealand SA White SA Non -White Italy Sri Lanka F η2

Emotional Expression 9 0.117d, e 0.158 a, b, e, g 0.106 e, g 0.104 e, g 0.216 c 0.218 c 0.127 29.762 0.04

Understanding Emotions External 20 0.172 d, g 0.242 b, c 0.166 g 0.139 e, g 0.198 c 0.219 c 0.155 11.502 0.01

Emotional Management Self 7 0.135 d, e, g 0.179 a, b, e 0.114 e, g 0.120 e, g 0.237 c 0.193 0.156 14.944 0.02

Emotional Management Others 5 0.098 d, e, g 0.158 a, b, e 0.078 e, g, f 0.092 e, g 0.208 c 0.186 c 0.122 30.759 0.04

Emotional Control 9 0.169 d, e, g 0.213 a, e 0.154 e, g 0.176 e, g 0.275 c 0.242 c 0.169 18.395 0.02

Table 41:

Acquiescence Index

Table 42:

Extreme Response Index

NOTE: Bonferroni test, p<0.05.

a. The score is significantly higher than SA White.

b. The score is significantly higher than SA non White.

c. The score is significantly higher than Italy.

d. The score is significantly higher than Sri Lanka.

e. The score is significantly lower than Sri Lanka

f. The score is significantly lower that Italy

NOTE: Bonferroni test, p<0.05.

a. The score is significantly higher than New Zealand.

b. The score is significantly higher than SA White.

c. The score is significantly higher than Sri Lanka.

d. The score is significantly lower that USA.

e. The score is significantly lower that SA Non-White.

f. The score is significantly lower that Sri Lanka

g. The score is significantly lower that Italy.

141

The procedure described in Van Herk et al. (2004) was used to compute the ERS and ARS

indices. To calculate the ERS index the frequencies of responses in the 1 and 5 response

categories (per respondent) over the different samples were counted. The number was

divided by the number of items in scale. The ERS index range is from 0.00 to 1.00. A

similar procedure was used to calculate the ARS index. The frequencies of responses in the

two highest categories on the rating scale (4 and 5) were counted and subtracted from the

number of clearly negative scores (two lowest categories, 1 and 2). The number was

divided by the number of items and resulted in an index with a range from -1.00 to 1.00.

The results are presented in tables 41 and 42. The discussion follows in section 4.5.7.

Verbal ability / bilingualism of respondents (item keying effects)

In an attempt to salvage the problem of acquiescence, positively and negatively keyed items

is used (Watson, 1992; Wong et al., 2003). However, this is known to lead to

corresponding method factors. That is, the inclusion on such items can ‘distort’ the factor

structure of an inventory, with the effect that the positively and negatively keyed items

form their own factors (Williams, Ford, & Nguyen, 2002).

In order to investigate the possible effect of verbal ability (i.e. bilingualism of respondents)

on the MI results, a model with negatively and positively keyed factors (M2b) was fitted to

the data from all the countries with bilingual respondents, as well as the Australian data (for

comparison purposes). The two method factors were allowed to correlate (Van de Vijver,

personal communication, 2008). The results are presented in table 43. For the Sri Lanka

analyses, only respondents that indicated they converse primarily in Sinhala (n=188) were

included in this analysis31

. M2a results are included for comparison purposes, with

measurement model M2b results.

31

Estimation problems were encountered and no admissible solution for the full Sri Lanka sample (n=587)

could be obtained.

142


df


(close)

Australia A

M2a

9104.71*

7676.40*

1165

6.59

0.94

0.94

0.042

0.074

0.059

(0.058; 0.060)

0.00

M2b 4534.16* 3868.19* 1114 3.47 0.97 0.98 0.022 0.039 0.039

(0.037; 0.041)

1.00

Australia B

M2a

8437.88*

6823.30*

1165

5.86

0.95

0.95

0.040

0.071

0.055

(0.054; 0.056)

0.00

M2b 4132.46* 3376.80* 1114 3.03 0.98 0.98 0.022 0.039 0.036

(0.034; 0.037)

1.00

Sri Lanka

Sinhala

M2a

2450.42*

1965.16*

1165

1.68

0.87

0.87

0.067

0.089

0.061

(0.056; 0.065)

0.00

M2b 1566.81* 1306.00* 1114 1.17 0.97 0.97 0.054 0.070 0.030

(0.023; 0.037)

1.00

SA White

M2a

6072.63*

4627.44*

1165

3.97

0.80

0.81

0.10

0.13

0.10

(0.098; 0.10)

0.00

M2b 1914.78* 1509.05* 1114 1.35 0.98 0.98 0.046 0.060 0.035

(0.030; 0.039)

1.00

SA Non-White

M2a

5503.49*

4043.73*

1165

3.47

0.87

0.88

0.13

0.12

0.086

(0.083; 0.089)

0.00

M2b 2057.18* 1606.74* 1114 1.44 0.98 0.98 0.063 0.056 0.036

(0.032; 0.040)

1.00

A correlation of 0.29 (p<0.05) between the negative and positively keyed method factors in

the South African White data emerged. The results revealed that for the South African non-

White analysis, this correlation increased to r=0.48 (p<0.05), whilst a similar correlation of

r=0.50 (p<0.05) between the two method factors emerged in the Sinhala analysis. A

different picture emerged with the Australian analysis. Here the correlation between the

positive and negatively keyed method factors was 0.81 (p<0.05) in sample A and 0.84

(p<0.05) in sample B. This confirms the finding by Gignac (2005) where he reports an r =

0.74 (p<0.001) between the negatively keyed and positively keyed method factors modeled

as part of the SUEIT nine factor structure with 64 items in an Australian sample. Tables 44

– 47 contain the factor loadings obtained in all the analyses.

NOTE: S-Bχ2, Sattora-Bentler Scaled Chi-square; NNFI, non-normed fit index; CFI, comparative fit index; RMSR, root



* p < 0.05

Table 43:

CFA results for models M2a and M2b in Australian, Sri Lanka (Sinhala) and South African samples

143

Item EE UEX EMS EMO EC POS NEG

7 0.32 0.34

10 0.81 -0.06

14 0.20 0.41

20 0.18 0.53

26 0.60 0.18

32 0.10 0.69

37 0.68 0.00

42 0.29 0.37

61 0.80 0.10

1 0.57 0.26

5 0.54 0.24

8 0.44 0.59

13 0.57 0.28

16 0.41 0.40

17 0.49 0.40

22 0.36 0.45

23 0.05 0.72

27 0.36 0.61

29 0.28 0.54

33 0.10 0.70

34 0.57 0.42

38 0.29 0.42

43 0.39 0.39

45 0.53 0.37

48 0.51 0.42

52 0.32 0.55

56 0.55 0.28

59 0.35 0.65

63 0.45 0.29

2 0.31 0.40

11 0.47 0.53

49 0.52 0.58

54 0.44 0.53

57 0.48 0.59

60 0.38 0.60

64 0.03 0.72

15 0.53 0.44

28 0.42 0.45

39 0.28 0.64

44 0.36 0.49

58 0.25 -0.04

4 0.47 0.33

6 0.28 0.40

9 0.27 0.64

19 0.12 0.42

25 0.42 0.57

31 0.58 0.58

36 -0.03 0.67

41 0.44 0.49

46 0.17 0.64

Table 44

Summary of RML completely standardised parameter estimates for model M2b for the SA White sample


144


7 0.30 0.39

10 0.66 0.09

14 0.14 0.44

20 0.23 0.55

26 0.43 0.36

32 0.06 0.63

37 0.64 0.26

42 0.22 0.42

61 0.73 0.18

1 0.40 0.43

5 0.47 0.35

8 0.35 0.39

13 0.50 0.50

16 0.15 0.51

17 0.37 0.57

22 0.17 0.53

23 0.04 0.67

27 0.21 0.45

29 -0.07 0.65

33 0.02 0.68

34 0.14 0.62

38 0.06 0.45

43 0.21 0.68

45 0.44 0.43

48 0.14 0.58

52 0.15 0.60

56 0.51 0.35

59 0.28 0.67

63 0.40 0.34

2 0.20 0.42

11 0.46 0.52

49 0.31 0.61

54 0.31 0.42

57 0.52 0.67

60 0.28 0.73

64 0.03 0.44

15 0.04 0.58

28 0.24 0.55

39 -0.01 0.66

44 0.25 0.52

58 0.01 0.11

4 0.37 0.36

6 0.38 0.51

9 0.32 0.59

19 0.32 0.44

25 0.18 0.65

31 0.38 0.64

36 -0.01 0.64

41 0.28 0.47

46 0.30 0.60

Table 45

Summary of RML completely standardized parameter estimates for model M2b for the SA Non-White sample


145


7 0.46 0.53

10 0.69 -0.04

14 0.41 0.58

20 0.34 0.56

26 0.51 0.14

32 0.19 0.66

37 0.66 -0.17

42 0.47 0.53

61 0.80 0.02

1 0.43 0.31

5 0.57 0.21

8 0.49 0.36

13 0.49 0.25

16 0.51 0.33

17 0.52 0.36

22 0.56 0.36

23 0.13 0.43

27 0.57 0.38

29 0.27 0.48

33 0.23 0.55

34 0.53 0.41

38 0.33 0.34

43 0.43 0.41

45 0.51 0.33

48 0.48 0.43

52 0.35 0.41

56 0.55 0.27

59 0.46 0.51

63 0.52 0.25

2 0.33 0.49

11 0.50 0.42

49 0.59 0.48

54 0.61 0.47

57 0.53 0.53

60 0.47 0.52

64 0.13 0.55

15 0.44 0.49

28 0.43 0.40

39 0.37 0.54

44 0.30 0.49

58 0.15 0.38

4 0.56 0.29

6 0.32 0.42

9 0.38 0.52

19 0.31 0.38

25 0.45 0.26

31 0.62 0.45

36 0.15 0.52

41 0.62 0.38

46 0.32 0.43

Table 46

Summary of RML completely standardized parameter estimates for model M2b for the Australia A sample


146


7 0.19 0.56

10 0.47 0.28

14 0.01 0.65

20 0.12 0.47

26 0.30 0.28

32 -0.03 0.63

37 0.56 0.20

42 0.07 0.56

61 0.60 0.34

1 0.20 0.37

5 0.21 0.47

8 0.19 0.47

13 0.26 0.53

16 0.05 0.17

17 0.27 0.44

22 0.44 0.50

23 0.28 0.54

27 0.61 0.33

29 0.16 0.55

33 0.32 0.49

34 0.32 0.32

38 0.20 0.45

43 0.60 0.33

45 0.30 0.42

48 0.02 0.35

52 0.30 0.53

56 0.15 0.43

59 0.18 0.54

63 0.38 0.28

2 0.19 0.32

11 0.35 0.43

49 0.66 0.35

54 0.31 0.30

57 0.39 0.36

60 0.42 0.54

64 0.21 0.60

15 0.27 0.32

28 0.02 0.33

39 0.65 0.50

44 0.17 0.52

58 -0.17 0.29

4 0.28 0.30

6 0.46 0.21

9 0.36 0.39

19 0.38 0.24

25 0.42 0.30

31 0.37 0.50

36 0.33 0.34

41 0.44 0.35

46 0.29 0.31

Table 47

Summary of RML completely standardized parameter estimates for model M2b for the Sri Lanka (Sinhala)

sample


147

4.5.7 Discussion: method bias

Response styles: ERS and ARS indices

The validity of cross-cultural comparisons may be greatly distorted by systematic

differences in response styles between countries. The results of the investigation into the

national cultural response styles were presented in section 4.5.6. Indices were calculated for

all the countries. This discussion will focus on all the results, as well as the specific

hypotheses that were investigated.

ERS

The results in tables 41 and 42 provide evidence that there are differences in response styles

between the different cultural groups. That is, significant differences between cultural

groups (F test, p<0.05) on both ERS and ARS emerged. For the ERS results (table 42) the

average η2

is 0.026 (ranging from 0.01 to 0.04). This is a small effect (Cohen, 1988).

Overall, it would seem that the results suggest that although there are ERS differences over

the different cultural groups, these differences are negligible. Previous research by Johnson

et al., (2005) reports a positive relation between ERS and Power Distance over 19 nations.

They argue that, “…extreme response style serves the goals of achieving clarity, precision,

and decisiveness in one’s explicit verbal statements, characteristics that are valued in

masculine and high Power Distance cultures” (Johnson et al., 2005, p.273). It was argued

that Sri Lankan respondents subscribe to the cultural value dimension of high Power

Distance and would exhibit more ERS.

Hence, it was hypothesised:

Hypothesis 2: Method bias (i.e. ERS) will have the most pronounced effect on the

transportability of the SUEIT when applied within the Sri Lankan sample.

This hypothesis (2) was not supported. The pattern of ERS in the Sri Lanka data did not

differ much from the pattern obtained for the Western countries. Hui and Triandis (1989)

showed that Hispanics showed more extreme responses when completing questionnaires in

Spanish as opposed to in English. They concluded that in studies using bilingual

respondents, extreme response ratings vary with the language of response used. All the Sri

148

Lankan respondents in this study were bilingual and answered the questionnaire in English.

This may be a possible explanation for the absence of ERS in the Sri Lanka results.

In addition, it was hypothesised that:

Hypothesis 3: Method bias (i.e. ERS) will not have an effect on the transportability of the


As expected, no substantial effect of ERS was evident in the Western countries. Hence,

hypothesis 3 is supported.

ARS

The differences in acquiescence indices are only meaningful if they account for a

substantive proportion of the variance (Cohen, 1988). The average η2

for the ARS results

(over the five EI subscales, reported in table 41) is 0.086 (ranging from 0.06 to 0.16) which

is considered to be a medium to large effect (>0.06 and <0.14) according to Cohen (1988).

It was hypothesised:

Hypothesis 4: Method bias (i.e. ARS) will have an effect on the transportability of the


The results clearly indicate that respondents from the three Western Anglo cultures

(Australia, USA and New Zealand) which are most similar in terms of Individualism and

Power Distance exhibited similar ARS patterns. That is, more ARS was exhibited by these

respondents than the Italian and South African non - White respondents. Hence, hypothesis

4 is supported.

Although no specific prediction regarding the South African White sample was made, a

similar trend to the other Western Anglo cultures was expected. This was not the case.

149

It was also hypothesised:

Hypothesis 5: Method bias (i.e. ARS) will not have an effect on the transportability of the

SUEIT when applied within the Sri Lankan sample.

The results revealed that Sri Lanka exhibited the closest ARS pattern to the three Western

cultures. Hence, hypothesis 5 is not supported.

EI Subscale Individualism Power Distance

ARS ERS ARS ERS

Emotional Expression 0.177** -0.088** -0.147** 0.055**

Understanding Emotions External 0.282** 0.003 -0.256** -0.021

Emotional Management Self 0.184** -0.058** -0.125** 0.061**

Emotional Management Others 0.110** -0.091** -0.033* 0.079**

Emotional Control 0.194** -0.043** -0.156** 0.031*

From table 48 it is clear that there are numerous significant weak relationships between the

Hofstede Individualism and Power Distance dimensions and the ERS and ARS indices.

Similar patterns are observed over all the subscales in the SUEIT. No notable relationships

(all correlations below 0.1) exist between Power Distance and ERS which is a replication of

the Johnson et al., (2004) findings for the Western countries. However, Johnson et al.,

(2004) report a positive relationship between high Power Distance and ERS. This was

finding was not replicated in this study. According to Johnson et al., (2004) ERS is not

related to Individualism. This finding is replicated in this study as negligible significant

correlations between ERS and Individualism emerged. In addition, Johnson et al., (2004)

report a negative relationship between Individualism and ARS. However, in the present

study weak positive correlations between ARS and Individualism (correlations ranging

from 0.110 to 0.282) exist. In addition, weak negative relationships between ARS and

Power Distance emerged. This confirms the Johnson et al., (2004, p. 272) finding,

“…contrary to our expectations, persons in high Power Distance countries were less likely

to engage in acquiescent responding”.

Table 48

Pearson correlations between Hofstede dimensions (IND, PD) and ARS and ERS indices over all the samples

Note: *p < 0.05, **p < 0.001

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The aim of investigating ERS and ARS in this study was twofold. First, uncovering method

bias has practical implications. That is, if left unevaluated, it may be misconstrued as

substantive differences in the latent construct (EI). Generally it is believed that

measurement and predictive bias is consistent. Measurement bias refers to sources of

irrelevant variance that result in systematically higher or lower scores for members of

particular groups. Predictive bias refers to the usefulness, validity and fairness of the test

for the purpose for which it was designed, and is found when for a given subgroup,

consistent nonzero errors of prediction are made for members of the subgroup (Cleary,

1968). It is generally believed that evidence of predictive invariance should be regarded as

supportive evidence for measurement invariance, and vice versa32

. A lack of MI evidence

(due to the undetected presence of method bias) may result in adverse impact. Adverse

impact is an important issue in personnel selection. It occurs when a specific selection

strategy affords members of a specific group (e.g. cultural group) a lower likelihood to be

selected than members of another group. Personnel selection procedures would want to

minimse adverse impact to avoid litigation, and ensure equal job opportunities across

groups in the labour market (in proportion to the size of the groupings) (Theron, 2007).

Hence, uncovering measurement bias may be an important step to assist in attaining this

goal.

Secondly, the investigation of these response styles as a type of method bias had a

theoretical grounding. That is, the aim was to seek replication of previous research on

culturally driven response styles. This would continue to inform on the cultural variation of

response styles. However, a definite limitation of the results is that the empirical

comparisons between the response style patterns, involved a comparison of only 6 different

cultural groups. Hence, the ability to generalise from this evidence remains severely

limited. More conclusive verification regarding the presence of these response styles will

only be available upon examination of a larger multinational dataset. It should be noted that

the main goal, however, was to investigated method bias, so as to judge the transportability

32

Although this is viewpoint widely supported, it should be noted that Millsap (1995) has proposed the

Duality Theorem, which states that not only are there are exceptions to the consistency rule (i.e. the two forms

of bias or invariance are consistent), but that inconsistency is, in fact, the rule (for a discussion, see Millsap,

1995).

151

of the instrument to various cultures. The replication of previous research regarding cultural

driven response styles, and the exploration of this in the South African data, was a

complementary goal in this study.

The findings reported here endeavoured to expand current knowledge on the relation

between culture and response styles. On a practical level it suggests that the effects of

cultural driven response styles may not be a big source of method bias in the current data.

Although the results are based on a small sample of cultures, two characteristics of the

results may be considered as valuable. First, much of the surveyed research on culture and

response styles has been conducted with mainly big European datasets. For example, the

Van Herk et al., (2004) study included three Mediterranean and three Northwestern

European countries. Cheung and Rensvold (2000) conducted an ERS and ARS SEM

illustrative example with USA and Italian data. Johnson et al., (2004) reported on the

relationships between culture and ERS and ARS over 19 countries. No African data was

included and the authors pointed out that, “…Europe is somewhat overrepresented, with

data contributed from 9 countries” (Johnson et al., 2004, p.268). The current investigation

of response styles in the South African data is a first investigation of this kind33

. Secondly,

the results seem to suggest that these specific response styles may not be a confounding

method bias factor for these cultural groups (based on this data). That is, the response styles

of the White and non-White South Africans were not drastically different from the other

groups included in this study – which is contrary to an intuitive expectation. That is, if it is

argued that the non-White respondents subscribe to a typical non-Western collectivistic,

high Power Distance cultural orientation, then one would expect some evidence of the

accompany response style (e.g. ARS). However, the Sithole (2001) cultural dimension

scores for non-White South Africans indicate that this group have a much more

individualistic orientation than is generally thought. This may be due to the effects of social

mobility theory in this group (Cutright, 1968; Miller, 1960). That is, an increase in wealth

is said to facilitate the movement from a collectivistic to individualistic cultural value

orientation. Considering the fact that workplace samples were used here, it may be assumed

33

No published study with South African data could be located which used the Van Herk et al., (2004)

methodology for calculating ERS and ARS.

152

that the surveyed non-White respondents already have an above average income compared

to the group norm. That is, they are probably not representative of the majority of non-

White South Africans and their current socio economic status. They may, however, be

representative of then non-White working population in South Africa. In this instance, the

practical implications of the current results are useful. Response styles may not be a

significant source of method bias in this group’s data. The theoretical explanation power of

the results from a cultural perspective, however, remains limited if it is argued that the non-

White group is in fact another ‘sub-cultural’ group shaped by their level of affluence (i.e.

more opportunities for tertiary education, entry into the labour market than what is

generally the norm for this group). It would seem that different ethnic groups in South

Africa are now not as purely advantaged or disadvantaged as before 1994 (the end of the

Apartheid era). The effect of acculturation and assimilation into Western culture may also

influence the extent to which individuals (e.g. non-White South Africans) identify with the

cultural origin of their ethnic group (Marsella, Dubanoski, Hamada, & Morse, 2000).

In a similar vein to the non-White results, the results of the White group were also contrary

to the general expectation. White South Africans are grouped in the Anglo Western cultural

cluster (Gupta & Hanges, 2004). Other societies in this cluster include Australia, New

Zealand, USA, Canada and England. More convergence in terms of the response style

results between the South African White data, and these Western cultures was therefore

expected. The Sri Lankan results also did not confirm the expected hypotheses (i.e

hypothesis two and five). No notable response style effects were evident. These results

may, in part, be explained in terms of the cultural accommodation hypothesis (Ralston,

Cunniff, & Gustafson, 1995). According to Yang and Bond (1980) cultural attitudes and

values associated with a language is acquired when an individual masters a second

language. Hence, bilingual subjects develop a mindset, partially shaped by the values

associated with the second-language culture (Bond & Yang, 1982). Consequently, it has

been argued individuals will respond in a manner that favours or accommodates the culture

associated with the language of presentation. More specifically, bilingual individuals (as is

the case with the South African White, non-White and Sri Lankan data) when responding to

a survey instrument in a secondary language, will accommodate the culture associated with

153

the secondary language (i.e. English). Ralston et al., (1995) report findings that support

notion. In their study Hong Kong Chinese managers responding to the English version of

the Schwartz Value Survey, scored higher on all five of the Western culturally important

values, compared to the Hong Kong Chinese managers that used the Chinese language

version of the test. They conclude that, “the language in which an instrument is

administered may produce ‘culturally accommodating’ responses that can affect the results

of a cross-cultural study” (Ralston et al., 1995, p.714). Hence, the hypothesised effects (in

this study) may not have been observed due to the fact that second language testing was

conducted. That is, due to responding in English and not Afrikaans, an African language,

Sinhala or Tamil, the respondents may have drawn more on a Western cultural frame of

reference, than their native culture (associated with their native language). This may have

‘neutralised’ the effects of culturally driven response styles, in these results. More research

is needed in this domain, specifically in the South African context. It will assist in a better

understanding of whether, and how culture drives response styles which may influence test

results.

In conclusion, it would seem that the ERS and ARS results do not suggest a strong presence

of this type of response style method bias in the current data. Hence, another possible

source of method bias, which may account for the lack of metric invariance, was

investigated.

Verbal ability / bilingualism of respondents (item keying effects)

The effect of respondent’s verbal ability on the MI results was investigated by fitting a

model with negatively and positively keyed factors (M2b) to the data from all the countries

with bilingual respondents. For comparison purposes, the model was also fitted to the

Australian data.

The following hypothesis was investigated:

Hypothesis 6: Method bias (i.e. due to verbal ability of respondents) will have the most

pronounced effect on the transportability of the SUEIT when applied within the Sri Lankan,

South African White and Non-White samples.

154

The results are consistent and fairly convincing (refer to table 43). By adding the two

method factors to the measurement model, the model fit in all the samples with bilingual

respondents improved considerably (in comparison with M2a results where no method

effects were modeled). In all the cases (M2b results), close fit was achieved (i.e. p(close

fit)>0.05). The biggest method bias effect is noted in the South African data. For example,

the RMSEA dropped drastically in the South African White analyses from 0.10 (M2a) to

0.035 (M2b), and from 0.086 (M2a) to 0.036 (M2b) in the South African non-White

analysis. The other model fit indices also consistently improved considerably. In fact,

evidence for very good model fit (e.g. NNFI and CFIs of 0.98, SRMR ≤ 0.060) was

obtained for both the South African datasets when the method effects were accounted for.

In a similar vein, the Sri Lanka results improved notably when the positively and negatively

keyed method effects were modeled (e.g. RMSEA dropped from 0.061 to 0.030, SRMR

from 0.089 to 0.070, NNFI and CFI increased drastically). The Australian results also

improved when the method effects were modeled (e.g. sample A: RMSEA dropped from

0.059 to 0.039, SRMR from 0.042 to 0.022 etc.). It is apparent, however, that modeling of

the method effects influenced the South African and Sri Lanka results much more than the

Australian results. This is seen in the consistent drastic improvements in model fit for these

groups (from M2a to M2b), relative to the improvements obtained in the Australian results

(for M2a and M2b). In addition, the correlation between the two method factors was small

(r=0.29) to moderate (r=0.49 and 0.50) for the groups with bilingual respondents. However,

a very strong correlation (r= 0.84 and 0.81) emerged between these factors in the

Australian data. This is interpreted as evidence that the positively and negatively keyed

method factors are more distinct entities in the data from the bilingual respondents (e.g.

Afrikaans speaking White South Africans) than the non-bilingual respondents (e.g.

Australians). It is concluded that method bias, due to the verbal ability of the bilingual

respondents in South Africa and Sri Lanka, has the most pronounced influence on the

transportability of the instrument to these groups. Therefore, hypothesis 6 is supported.

The common practice of using Western imported tests in multicultural, multilingual non-

Western societies (like South Africa or Sri Lanka), justifies the need for research on the

155

influence of language proficiency on test results. The majority of White, Black and

Coloured South Africans speak English as either a second or third language (Wallis & Birt,

2003). However, most psychological testing is done in English. The large number of

official languages in South Africa, limited availability of test administrators who speak

these languages and different dialects and lack of language standardisation, limits the

practice of test translation into native languages (Van Eeden, 2007). This does not,

however, negate the responsibility of the test user to prudently consider the possibility that

language proficiency may have on test results. According to the Society of Industrial and

Organisational Psychology of South Africa’s publication, Guidelines for the Validation and

Use of Assessment Procedures for the Workplace, whether use of an English test for second

or third language users is considered as fair, is judged on how it relates to the requirement

of the position. “For a position where the ability to read and understand material written in

English is critical and an inherent requirement of the job, use of such a test would be

considered fair, justifiable and legally defensible” (Guidelines for the Validation and Use of

Assessment Procedures for the Workplace, 2005, p. 47). Method bias, such as was

uncovered in this section due to testing conducted in a second language, however, if simply

ignored, may lead to invalid inferences if such effects are not accounted for. Group

membership in terms of language proficiency may influence the validity of test results.

4.5.8 Results: Differential Item Functioning

Two approaches were used to investigate the DIF of items contained in the SUEIT. First,

the 2-way analysis of variance (ANOVA) approach proposed by Van de Vijver and Leung

(1997) for detecting item bias was utilsed on all the items and datasets. This approach is

traditionally used in cross-cultural research. This was followed by the application of the

MACS DIF approach (Chan, 2000) described in section 4.4.4 to all the datasets. In all the

analyses, Australia was used as the reference group. All the items (50) included in model

M2a were subjected to the DIF analyses.

It should be noted that although these two methods / approaches share a similar purpose,

they are, “…vastly discrepant in terms of both their theoretical underpinning and their

statistical approach to the task” (Byrne & Watkins, 2003, p. 159). Although both zero in on

156

item scores relative to total scores, three main distinctions exist. Firstly, analysis of

covariance is focused on co-variation among item scores whilst ANOVA is focused on

differences in item score levels. Secondly, with ANOVA score level is not treated as a

latent trait as is the case with analysis of covariance. Thirdly, ANOVA results are based on

sample subgroups. These subgroups are calculated based on the Van de Vijver and Leung

(1997) methodology34

. Analysis of covariance is based on the entire sample for each culture

(Byrne & Watkins, 2003). Although these methods have the same goal, it was expected that

discrepancies in the results would exist. However, a similar pattern in the results was

expected. The traditional ANOVA approach was supplemented with the MACS DIF

approach, as the latter approach allowed for the comparison of latent mean differences,

after the DIF items were flagged and freed in the final partially constrained model.

4.5.8.1 Results: 2-way ANOVA DIF

With the ANOVA approach, an item is viewed as biased if eta-squared values of at least

0.02 for the uniform (group main effect) or non-uniform (interaction effect) components are

found (Pietersen, 2004; Van de Vijver, Personal communication, 2008). This low value was

used because of the overall low level of the effect sizes (Meiring et al., 2005) in the results.

In addition, an eta-squared value of 0.06 is taken to point towards a moderate effect size

(Van de Vijver, Personal communication, 2008). Tables 49 – 52 contain the results of the

ANOVA bias analyses.

34

With the ANOVA procedure, item score serves as the dependent variable and culture and score levels as

independent variables. Score levels are calculated by splitting the score distribution based on the score of all

cultural groups together, with the same cutoff scores being applied to all cultural groups. Cutoff points are

identified such that the number of respondents in each score group is approximately equal (Van de Vijver &

Leung, 1997).

157

Australia and New Zealand Australia and USA Australia and SA White

Item Uniform bias Non-uniform

bias

Uniform bias Non-uniform

bias


bias 1 0.000 0.006 0.000 0.011 0.001 0.003 2 0.000 0.008 0.001 0.001 0.001 0.006 4 0.003 0.000 0.001 0.001 0.012 0.000 5 0.001 0.009 0.000 0.012 0.002 0.000 6 0.001 0.004 0.001 0.003 0.004 0.001 7 0.002 0.003 0.000 0.005 0.002 0.001 8 0.000 0.008 0.000 0.001 0.004 0.017 9 0.001 0.013 0.000 0.002 0.016 0.000 10 0.004 0.002 0.000 0.005 0.000 0.009 11 0.002 0.003 0.005 0.002 0.002 0.006 13 0.000 0.006 0.000 0.001 0.002 0.004 14 0.006 0.003 0.003 0.000 0.001 0.013 15 0.000 0.000 0.002 0.000 0.002 0.003 16 0.000 0.001 0.000 0.005 0.009 0.002 17 0.004 0.002 0.000 0.006 0.015 0.006 19 0.001 0.003 0.001 0.000 0.004 0.012 20 0.000 0.004 0.000 0.001 0.004 0.004 22 0.000 0.008 0.000 0.005 0.003 0.001 23 0.000 0.005 0.002 0.001 0.005 0.015 25 0.008 0.001 0.000 0.007 0.000 0.006 26 0.002 0.001 0.000 0.004 0.000 0.003 27 0.001 0.001 0.004 0.002 0.001 0.011 28 0.001 0.001 0.003 0.005 0.000 0.003 29 0.000 0.004 0.001 0.004 0.017 0.015 31 0.000 0.006 0.000 0.003 0.002 0.002 32 0.001 0.005 0.000 0.009 0.004 0.007

NOTE: EDC and ER subscale items not included in the bias analyses. *Items with significant

(non)uniform bias at p < 0.05

Table 49

Uniform and non-uniform item bias effects for SUEIT (M2a) items 1-32 for New Zealand, USA and SA White data.

158

Australia and New Zealand Australia and USA Australia and SA White


bias


bias


bias

33 0.000 0.005 0.002 0.004 0.009 0.002

34 0.001 0.006 0.000 0.006 0.000 0.002

36 0.000 0.006 0.007 0.002 0.018 0.009

37 0.004 0.004 0.002 0.001 0.003 0.007

38 0.008 0.019 0.019 0.017 0.001 0.008

39 0.000 0.001 0.000 0.015 0.013 0.003

41 0.001 0.010 0.000 0.007 0.009 0.010

42 0.000 0.001 0.000 0.004 0.007 0.002

43 0.005 0.005 0.003 0.001 0.002 0.003

44 0.008 0.009 0.004 0.006 0.001 0.004

45 0.000 0.004 0.000 0.002 0.011 0.005

46 0.000 0.002 0.003 0.001 0.005 0.009

48 0.000 0.003 0.001 0.004 0.006 0.001

49 0.001 0.002 0.003 0.003 0.001 0.000

52 0.003 0.002 0.005 0.003 0.001 0.001

54 0.001 0.003 0.001 0.004 0.000 0.007

56 0.000 0.001 0.001 0.001 0.006 0.004

57 0.001 0.009 0.000 0.001 0.000 0.001

58 0.002 0.007 0.027* 0.002 0.002 0.001

59 0.000 0.001 0.001 0.004 0.001 0.002

60 0.001 0.004 0.000 0.001 0.004 0.007

61 0.004 0.001 0.000 0.002 0.001 0.001

63 0.006 0.012 0.004 0.009 0.001 0.004

64 0.001 0.010 0.003 0.004 0.000 0.005



Table 50

Uniform and non-uniform item bias effects for SUEIT (M2a) items 33 - 64 for New Zealand, USA and SA White data.

159

Australia and Italy Australia and Sri Lanka Australia and SA non-White


bias


bias


bias

1 0.041* 0.006 0.003 0.004 0.022* 0.010

2 0.025* 0.004 0.008 0.002 0.003 0.004

4 0.078* 0.009 0.003 0.001 0.001 0.007

5 0.003 0.019 0.001 0.006 0.012 0.002

6 0.012 0.007 0.043* 0.000 0.011 0.005

7 0.000 0.002 0.010 0.003 0.003 0.004

8 0.002 0.003 0.003 0.002 0.000 0.000

9 0.007 0.003 0.005 0.001 0.005 0.008

10 0.002 0.012 0.000 0.000 0.003 0.001

11 0.037 0.003 0.001 0.003 0.001 0.003

13 0.002 0.003 0.040* 0.005 0.005 0.009

14 0.013 0.004 0.004 0.001 0.001 0.002

15 0.015 0.005 0.000 0.002 0.010 0.003

16 0.023 0.003 0.066* 0.007 0.007 0.009

17 0.009 0.002 0.052* 0.005 0.007 0.001

19 0.035* 0.016 0.011 0.001 0.002 0.004

20 0.026* 0.002 0.006 0.001 0.015 0.006

22 0.012 0.008 0.001 0.001 0.011 0.003

23 0.000 0.009 0.007 0.009 0.000 0.021* 25 0.000 0.004 0.002 0.004 0.000 0.026*

26 0.054* 0.001 0.001 0.005 0.004 0.004

27 0.000 0.009 0.010 0.002 0.016 0.001

28 0.007 0.004 0.005 0.005 0.000 0.005

29 0.059* 0.003 0.018 0.003 0.002 0.003

31 0.034* 0.002 0.014 0.001 0.001 0.012

32 0.001 0.011 0.009 0.002 0.000 0.009



Table 51

Uniform and non-uniform item bias effects for SUEIT (M2a) items 1-32 for Italian, Sri Lanka and SA Non- White data.

160

Australia and Italy Australia and Sri Lanka Australia and SA non-White


bias


bias


bias

33 0.001 0.004 0.002 0.002 0.002 0.006

34 0.009 0.003 0.060* 0.001 0.002 0.000

36 0.088* 0.005 0.019 0.002 0.012 0.001

37 0.013 0.025* 0.014 0.001 0.001 0.009

38 0.000 0.003 0.001 0.002 0.008 0.005

39 0.001 0.014 0.015 0.006 0.016 0.006

41 0.009 0.013 0.000 0.005 0.008 0.013

42 0.005 0.001 0.001 0.008 0.000 0.003

43 0.018 0.002 0.058* 0.005 0.018 0.016

44 0.001 0.003 0.001 0.001 0.000 0.005

45 0.011 0.002 0.004 0.009 0.016 0.008

46 0.009 0.003 0.042 0.003 0.030* 0.011

48 0.009 0.000 0.000 0.010 0.002 0.001

49 0.045* 0.005 0.003 0.002 0.000 0.007

52 0.001 0.001 0.000 0.002 0.000 0.000

54 0.012 0.004 0.012 0.013 0.003 0.005

56 0.075* 0.019 0.000 0.003 0.003 0.002

57 0.000 0.001 0.000 0.002 0.001 0.017

58 0.027* 0.001 0.009 0.003 0.003 0.002

59 0.007 0.001 0.001 0.001 0.001 0.008

60 0.006 0.000 0.000 0.002 0.005 0.013

61 0.001 0.008 0.013 0.002 0.001 0.003

63 0.002 0.001 0.005 0.001 0.003 0.009

64 0.055* 0.006 0.001 0.003 0.004 0.008

4.5.8.2 Discussion: 2-way ANOVA DIF results

An item is unbiased if persons from different cultures with an equal standing on the

theoretical construct underlying the instrument have the same expected score on the item

level (Van de Vijver & Leung, 1997). In these results a significant main effect of the

culture group was taken to point to uniform bias. A significant interaction of score level and

culture interaction pointed to non-uniform bias. The results revealed that the highest

number of biased items emerged in the Italian data (16 items, 32%). With the exception of

one item (item 37), all of these biased items exhibited uniform bias. Four of the items

(items 4, 29, 36, and 56) obtained medium effect sizes. In the Sri Lanka data 7 uniform

biased items was identified (14%), of which two obtained medium effect sizes. In the South

African non - White data 2 non-uniform and 2 uniform biased items emerged (8%). None

obtained medium effect sizes. In the USA data, one uniform biased item was identified. No

biased items emerged in the New Zealand and South African White analyses.

Table 52

Uniform and non-uniform item bias effects for SUEIT (M2a) items 33-64 for Italian, Sri Lanka and SA Non - White data.



161

In relation to item bias, it was hypothesised that:





Partial35

support for hypothesis 7 was obtained (based on this approach to investigating

DIF). In the data from the Western Anglo cultures (New Zealand, USA and South Africa

White), with the exception of one item in the USA analysis, no bias was identified. A

distinction was evident with the non – Western cultures that all obtained some biased items.

However, contrary to the expectation that the Sri Lankan data would contain the most

biased items, more biased items emerged in the Italian data. The least number of biased

items (from the non – Western groups) emerged in the South African Non – White data.

Meiring et al., (2005) identified a ‘large proportion’ of biased items in the 15FQ+ over

various language groups in South Africa (36% of the 200 items). Despite this, they

concluded that item bias was not a major disturbance in the instrument, as only, “one item

showed a medium effect size” (Meiring et al., 2005, p. 5). A similar proportion of biased

items in the Italian data emerged (32%). However, here and in the other samples, only a

few items showed medium effect sizes (i.e. 4 in the Italian data, 2 in Sri Lanka). It may

therefore be concluded that (according to these results) item bias is not a major disturbance

in the SUEIT over the various cultural groups. Although the pattern in terms of number of

biased items does not match either of the CDR patterns completely (pattern 1, table 4, or

pattern 2, table 5), there seems to be a clear distinction between the Western versus Non -

Western results.

4.5.8.3 Results: Multiple-Group Mean and Covariance Structure Analyses (MACS) DIF

Table 10 provides a summary of the iterative model fitting strategy36

employed to run the

series of MACS models with Australia as the reference group. The results (individual group

35

Partial support was obtained as there was a clear trend in terms of more biased items emerging in the non-

Western (e.g. Italy) country analyses in comparison with the Western (e.g. New Zealand) countries. However,

the pattern confirmed neither of the CDR patterns completely. 36

Step four of the generic iterative model-fitting strategy (Chan, 2000) is to split the sample and do a

validation procedure. This step could not be conducted in this study due to sample size limitations.

162

comparisons between Australia and New Zealand, USA, Italy, South Africa White and non-

White data, and Sri Lanka for each of the five subscales) are presented in tables 53 - 59.

Because the DIF analysis assumes the unidimensionality37

of a given subscale (Chan,

2000), this assumption was tested as a first step in the analysis. Hence, a multi-group

single-factor CFA model in which factor loadings and intercepts were allowed to vary

freely (fully unconstrained model), except for those constraints imposed for identification

and scaling purposes, was fitted as a first step in the analyses. If the results revealed a

violation of the unidimensionality assumption, an EFA38

was conducted. Next, the fully

constrained model (model in which all corresponding factor loadings as well as item

intercepts were constrained to be equal across groups) was fitted to the data from both

groups. The size of the modification indices (MIs) associated with each item parameter was

then identified to flag items exhibiting DIF (Chan, 2000). In accordance with the procedure

37

It should be noted that the methodological ideal (based on the results in section 4.5.7) would have been to

model the positive and negatively keyed method factors in the DIF analysis. This was not possible as the

unidimensionality assumption, a necessary prerequisite for this procedure, would have been violated. In

addition, the goal was to uncover DIF in the instrument as it would manifest in its real world application. That

is, to establish to what extent the instrument may not be transportable to another culture. Generally, the effect

of method factors are not taken into account in inferences based on scores derived from a particular

instrument. 38

A series of EFA’s was conducted (Principle Axis Factoring with Direct Oblimin rotation, SPSS 15) on the

Emotional Expression and Emotional Control subscales, as the fully unconstrained CFA results over all the

samples did not obtain good fit (i.e. in all the analyses RMSEA >0.1, CFA and NNFI<0.90). Over all the

samples, The Emotional Expression subscale results revealed two substantive factors. Generally, items 7, 14,

20, 32, and 42 load unto the same factor (factor 1). The content of these items all assess verbal emotional self

expression at work (e.g. “I find it difficult to talk about my feelings with my colleagues”; “When I’m anxious

at work, I find it difficult to express this to my colleagues”). Items 10, 37, and 61 assess other’s perception

(factor 2) on an individual’s emotional expression (“Colleagues find it easy to pick-up on how I’m feeling”;

“Colleagues know when I am worried”). Item 26 (“I can portray how I’m feeling to colleagues through my

body language alone”) assesses a non-verbal aspect of emotional expression and hence did not load unto the

factor one. The first factor (emotional self expression) was used in the DIF analyses as most of the theoretical

arguments of how culture might affect emotional expression (i.e. through cultural display rules, the influence

of cultural dimensions like Uncertainty Avoidance and Collectivism) relates to emotional self expression. The

EFA results over all the samples for the Emotional Control subscale, revealed two factors. Generally, items 6,

9, 19, 36, and 46 load unto the same factor. These items all seem to measure different aspects of controlling

strong emotions like stress and anger in the workplace (“When I’m under stress, I tend to get irritated by

colleagues”, “I find it easy to control my anger at work”). Items 4, 25, 31, and 41 all measure the extent to

which an individual allows strong emotions, for example anxiety, being upset or excitement, to detract them

from their focus on a specific work-related task (“When I’m anxious, I can remain focused on what I’m

doing”, “I find it hard to concentrate on a task when I’m really excited about something”, I find it difficult to

think clearly when I’m feeling anxious about something at work”). The first factor was used for the DIF

analyses, as the arguments presented in chapter three regarding cultural display rules, and whether strong

emotions should be concealed (as is the norm in collectivistic societies) would more directly be testable

through the content of these items. That is because the content of the items more explicitly relates to

controlling strong emotions like anger, how easy the individual finds it to deal with this emotion (i.e. easy or

hard to overcome, control) as well as strategies to deal with these emotions (i.e. getting irritated or not,

thinking through what is causing the emotion).

163

stipulated by Chan (2000), the between-group equality constraint for the flagged item’s

factor loading (non-uniform DIF) was lifted. The model was refitted. Once again the MI

values for the factor loadings were inspected and the appropriate strategy followed (if

significant, the item loading was flagged and freed in the subsequent model). This process

was repeated, until the largest MI value was no longer significant39

, whereupon the MI

values associated with the item intercepts were inspected to detect uniform DIF. The same

iterative procedure was followed. According to Hair et al., (2006) partial metric and scalar

invariance is achieved, when at least two items do not display non-uniform or uniform DIF.

It thus follows that the iterative model fitting procedure was not continued (i.e. examining

the item intercepts) after examining the item slopes, if not at least two items were found to

not display non-uniform DIF. In some cases no items was found to display DIF. Hence, the

Satorra-Bentler chi-square difference between the fully constrained and fully unconstrained

models were (based on the S-B adjustment formula, Satorra & Bentler, 1999) calculated. A

non-significant difference points to full metric and scalar equivalence (supported by the

absence of any DIF items) and allows for meaningful latent mean comparisons (e.g.

Emotional Expression over Australia and New Zealand) on the subscale level, without

freeing any item. However, there were also three cases where no invariant items per

subscale could be found (i.e. the Emotional Management Self subscale over Australia and

Italy; Emotional Management Subscale over Australia and Sri Lanka; the Emotional

Control subscale over Australia and South Africa Non-White). Hence, no latent mean

comparisons between these groups on the respective subscales were possible.

In group comparisons where two or more invariant items were found, the latent means

(Kappa) were interpreted from the final partially constrained MACS model (PC Final). If

measurement instruments are at least partially invariant, valid cross-national comparisons

can be conducted even when the ideal of full invariance is not realised (Steenkamp &

Baumgartner, 1998). For the Australia and USA Emotional Management Others subscale,

no Kappa value was estimated. There was a non-significant difference between the fully

39

This procedure required that the MI values of the set of items on each scale were inspected multiple times

for statistical significance. Thus, a Bonferronni correction was employed to determine the alpha value at each

step of the iterative procedure.

164

constrained and unconstrained models40

.That is, all the items are invariant and hence no

Kappa value was estimated. In this case it is therefore possible to make meaningful direct

comparisons between the means of the Emotional Management Others subscale over

Australia and the USA.

Australia and New Zealand

The results of the Australia, New Zealand DIF analyses (table 53) indicate that there is no

evidence of DIF over the two groups for four of the five EI subscales. For these four

subscales each item (included in the original scale, or retained after the EFA analysis)

functions equivalently for both Australians and New Zealanders.

Model df χ2 S-Bχ2 MI ∆df ∆ S-Bχ2 NNFI CFI RMSEA (CI) P

(close)

EE

Fully Unconstrained 10 32.21* 27.03* - - - 0.96 0.98 0.086

(0.046; 0.013)

0.061

Fully Constrained 18 35.67* 31.31* - 8 3.39ns 0.98 0.98 0.056

(0.019; 0.089)

0.34

UEX


(0.041; 0.057)

0.58

Fully Constrained 378 653.06* 570.45 - 38 39.19ns 0.97 0.97 0.047

(0.039; 0.054)

0.75

PC Free λ5,2 377 645.13* 563.96 15.291 - - 0.97 0.97 0.046

(0.038 ; 0.054)

0.79

PC Free λ1,2 376 641.17* 561.05 9.782 - - 0.97 0.97 0.046

(0.038 ; 0.054) 0.80

PC Free τ38,2 375 636.43* 556.00 10.443 - - 0.97 0.97 0.046

(0.037 ; 0.053) 0.82

PC Final 375 636.43* 556.00 - 3 14.28* 0.97 0.97 0.046

(0.037 ; 0.053) 0.82

EMS


(0.044 ; 0.094) 0.098

Fully Constrained 40 70.25* 63.41* - 12 3.58 ns 0.99 0.99 0.050

(0.025 ; 0.073)

0.47

EMO

Fully Unconstrained 10 14.94ns 12.98ns - - - 0.98 0.99 0.036

(0.00; 0.084)

0.63

Fully Constrained 18 19.17ns 17.66ns - 8 4.21 ns 1.00 1.00 0.00

(0.00; 0.057)

0.90

EC

Fully Unconstrained 10 14.04 ns 13.49 ns - - - 0.99 0.99 0.039

(0.00; 0.086)

0.59

Fully Constrained 18 18.59 ns 17.82 ns - 8 4.34 ns 1.00 1.00 0.00

(0.00; 0.058)

0.90

40

The LISREL output for the constrained model did not estimate any non-zero modification indices for

Lamda-X or Tau-X.

Table 53

MACS DIF results: Australia (n=234) and New Zealand (n=234)

Note: EE = Emotional Expression (5 items); UEX = Understanding Emotions External; EMS = Emotional Management

Self; EMO = Emotional Management Others; EC = Emotional Control (5 items); *p < 0.05; 1: p<0.0025; 2: p<0.0026; 3:

p<0.0027; ns = non significant

165

In the Understanding Emotions External subscale, only two items displayed non-uniform

(10%) and one item, uniform DIF (5%) (table 53). Overall, all the models obtained good

model fit (NNFI and CFI > 0.95; RMSEA <0.08; and p(close fit)>0.05). The proportion of

non-uniform biased items for the entire instrument was 4%, whilst only 2% of the items

exhibited uniform bias.

Australia and USA

The results of the Australia and USA analyses (table 54) revealed all items contained in the

Emotional Management Others subscale to be invariant. The Emotional Management Self

subscale (which contains 7 items) obtained the highest proportion of biased items,

compared to the other subscales. Two items (28.5%) displayed non-uniform bias and 1 item

(14.28%) uniform bias. Analyses of the Understanding Emotions External subscale

revealed 3 non-uniform (15%) and 2 uniform bias items (10%). Over all the five subscales,

12% of the items displayed non-uniform bias and 8% uniform bias. As with the New

Zealand analyses, all the model fit results were good (NNFI and CFI > 0.95; RMSEA

<0.08; and p(close fit)>0.05). One exception is the RMSEA obtained for the Emotional

Expression subscale (>0.1). It should be noted, however, that this subscale consistently

obtained over most of the other samples, for the fully unconstrained model, RMSEA values

outside of the <0.08 range (e.g. 0.086 in Italy; 0.085 for Sri Lanka). As expected, the model

fit increased gradually as flagged items was freed. In all the cases, the final partially

constrained model exhibited very good fit (NNFI and CFI > 0.96; p(close fit)>0.05; RMSEA <

0.06 – except for the Emotional Expression subscale).

166


(close)

EE


(0.085; 0.15)

0.0004


(0.069; 0.12)

0.0026

PC Free λ32,1 17 67.24* 58.36* 11.021 - - 0.96 0.97 0.092

(0.067; 0.12)

0.0040

PC Final 17 67.24* 58.36* - 1 4.31* 0.96 0.97 0.092

(0.067; 0.12)

0.96

UEX


(0.046; 0.060)

0.23

Fully Constrained 378 801.98* 677.89* - 38 64.62* 0.94 0.98 0.053

(0.046; 0.059)

0.24

PC Free λ5,2 377 794.16* 673.61* 17.353 - - 0.98 0.98 0.052

(0.046; 0.059)

0.26

PC Free λ29,2 376 791.96* 672.15* 10.724 - - 0.98 0.98 0.052

(0.046; 0.059)

0.26

PC Free λ22,2 375 784.00* 665.31* 10.745 - - 0.98 0.98 0.052

(0.046; 0.058)

0.30

PC Free τ 38,2 374 769.40* 650.58* 31.803 - - 0.98 0.98 0.051

(0.044; 0.057)

0.41

PC Free τ 27,2 373 765.24* 646.25* 9.644 - - 0.98 0.98 0.051

(0.044; 0.057)

0.43

PC Final 373 765.24* 646.25* - 5 33.30* 0.98 0.98 0.051

(0.044; 0.057)

0.43

EMS


(0.037; 0.083)

0.21


(0.042; 0.079)

0.16

PC Free λ60,3 39 87.67* 76.83* 10.356 - - 0.98 0.98 0.058

(0.039; 0.077)

0.23

PC Free λ57,3 38 83.13* 72.90* 8.897 - - 0.98 0.99 0.057

(0.037; 0.076)

0.27

PC Free τ 49,3 37 77.06* 67.10* 13.906 - - 0.99 0.99 0.053

(0.032; 0.073)

0.37

PC Final 37 77.06* 67.10* - 3 16.12* 0.99 0.99 0.053

(0.032; 0.073)

0.37

EMO


(0.031; 0.10)

0.18

Fully Constrained 18 23.95ns 22.40 ns - 8 0.00 ns 0.99 0.99 0.029

(0.0; 0.063)

0.82

EC


(0.027; 0.10)

0.23


(0.033; 0.089)

0.23

PC Free τ 36,3 17 38.76* 30.19* 14.721 - - 0.98 0.98 0.052

(0.018; 0.082)

0.42

PC Final 17 38.76* 30.19* - 1 9.23* 0.98 0.98 0.052

(0.018; 0.082)

0.42

Note: EE = Emotional Expression (5 items), UEX = Understanding Emotions External, EMS = Emotional Management

Self; EMO = Emotional Management Others; EC = Emotional Control (5 items); *p < 0.05; 1: p<0.01; 2: p<0.0125; 3:

p<0.0025; 4: p<0.0026; 5: p<0.0027; 6: p<0.007; 7: p<0.0083;

Table 54

MACS DIF results: Australia (n=287) and USA (n=287)

167

Australia and Italy

In the Australian and Italian analyses (refer to tables 55 and 56), all of the subscales

contained some non-invariant items. More specifically, no invariant items could be

identified for the Emotional Management Self subscale and hence no results are reported

for this subscale. The results for the Understanding Emotions External subscale revealed 10

non-uniform (50%) and 8 uniform (40%) biased items. Apart from the Emotional

Management Self subscale, this was the highest proportion of both types of identified bias

in one subscale. Overall, with the items from the Emotional Management Self subscale

included, 19 non-uniform (38%) and 18 (36%) uniform biased items emerged. In all cases

where the non-invariant items were freed, the final partially constrained MACS model

exhibited evidence of very good model fit (NNFI and CFI > 0.96; p(close fit)>0.05; RMSEA <

0.07).


(close)

EE


(0.055; 0.12)

0.031


(0.086; 0.13)

0.00

PC Free τ 20,1 17 53.01* 44.81* 90.941 - - 0.97 0.97 0.072

(0.046; 0.098)

0.076

PC Free τ 7,1 16 48.41* 40.41* 10.442 - - 0.97 0.98 0.069

(0.043 ; 0.096)

0.11

PC Final 16 48.41* 40.41* - 2 71.65* 0.97 0.98 0.069

(0.043 ; 0.096)

0.11

Note: EE = Emotional Expression (5 items), *p < 0.05; 1: p<0.01; 2: p<0.0125; ns= non significant; PC=Partially Constrained

model

Table 55

MACS DIF results: Australia (n=320) and Italy (n=320) for EE

168


(close)

UEX


(0.039; 0.052)

0.85


(0.059; 0.070)

0.00

PC Free λ1,2 377 1061.31* 855.48* 45.243 - - 0.94 0.94 0.063

(0.057; 0.069)

0.00

PC Free λ5,2 376 1040.59* 838.10* 42.594 - - 0.94 0.94 0.062

(0.056; 0.068)

0.00

PC Free λ 56,2 375 1027.24* 822.99* 21.915 - - 0.94 0.95 0.061

(0.056; 0.067)

0.00

PC Free λ 16,2 374 1007.46* 805.12* 23.466 - - 0.95 0.95 0.060

(0.054; 0.066)

0.002

PC Free λ 33,2 373 986.51* 787.99* 24.87 - - 0.95 0.95 0.059

(0.053; 0.065)

0.005

PC Free λ 59,2 372 963.40* 769.79* 21.148 - - 0.95 0.95 0.058

(0.052; 0.064)

0.013

PC Free λ 22,2 371 948.33* 756.46* 18.99 - - 0.95 0.95 0.057

(0.051; 0.063)

0.024

PC Free λ 52,2 370 933.28* 745.73* 19.8510 - - 0.95 0.95 0.056

(0.051; 0.062)

0.036

PC Free λ 8,2 369 921.71* 738.31* 17.9111 - - 0.95 0.95 0.056

(0.050; 0.062)

0.047

PC Free λ 17,2 368 912.59* 729.39* 12.3012 - - 0.95 0.96 0.055

(0.050; 0.061)

0.063

PC Free τ 56,2 367 844.28* 672.21* 130.813 - - 0.96 0.96 0.051

(0.045; 0.057)

0.38

PC Free τ 29,2 366 805.07* 637.92* 65.1614 - - 0.97 0.97 0.048

(0.042; 0.054)

0.67

PC Free τ 16,2 365 787.46* 623.31* 34.5615 - - 0.97 0.97 0.047

(0.041; 0.053)

0.77

PC Free τ 17,2 364 776.60* 613.95* 27.616 - - 0.97 0.97 0.046

(0.040; 0.053)

0.82

PC Free τ 1,2 363 767.93* 606.03* 19.8617 - - 0.97 0.97 0.046

(0.039; 0.052)

0.86

PC Free τ 63,2 362 759.64* 598.34* 16.3818 - - 0.97 0.97 0.045

(0.039; 0.052)

0.89

PC Free τ 22,2 361 751.23* 590.24* 15.619 - - 0.97 0.97 0.045

(0.038; 0.051)

0.92

PC Free τ 59,2 360 748.51* 586.72* 13.6620 - - 0.97 0.97 0.044

(0.038; 0.051)

0.92

PC Final 360 748.51* 586.72* - 18 703.05* 0.97 0.97 0.044

(0.038; 0.051)

0.92

EMO

Fully Unconstrained 10 16.82ns 13.58 ns - - - 0.97 0.99 0.034

(0.0; 0.074)

0.7


(0.017; 0.076)

0.5

Free λ58,4 17 20.85 ns 17.54 ns 33.5321 1 17.80* 1.00 1.00 0.01

(0.0; 0.052)

0.94

EC

Fully Unconstrained 10 33.87* 27.50* _ _ _ 0.95 0.98 0.074

(0.042; 0.11)

0.10

Fully Constrained 18 108.40* 99.38* _ 8 81.48* 0.88 0.89 0.12

(0.097; 0.14)

0.00

PC Free λ 19,5 17 108.15* 99.05* 6.7321 _ _ 0.87 0.89 0.12

(0.10; 0.15)

0.00

PC Free τ 19,5 16 43.42* 38.04* 128.121 - - 0.96 0.97 0.066

(0.039; 0.093)

0.15

PC Final 16 43.42* 38.04* - 2 94.80* 0.96 0.97 0.066

(0.039; 0.093)

0.15

Note: UEX = Understanding Emotions External, *3: p<0.0025; 4: p<0.0026; 5: p<0.0027; 6: p<0.0029; 7: p<0.0031; 8:

p<0.0033; 9: p<0.0035; 10: p<0.0038; 11: p<0.0041; 12: p<0.0045; 13: p<0.0025; 14: p<0.0027; 15: p<0.0029; 16: p<0.0031;

17: p<0.0033; 18: p<0.0035; 19: p<0.0038; 20: p<0.0041; 21: p<0.01; ns= non significant; PC=Partially Constrained model

Table 56

MACS DIF results: Australia (n=320) and Italy (n=320) for UEX, EMO and EC

169

Australia and South Africa White

The South African EFA results for the Emotional Control subscale revealed that item 19 (“I

find it easy to control my anger at work”) did not obtain a sufficient loading (0.187) on the

first factor (measuring control of strong emotions like stress and anger in the workplace).

However, a strong loading (0.665) was obtained in the Australian data for this item on this

factor. Consequently, the item was eliminated from the Emotional Control item pool for the

DIF analysis.

The results of the Australian and South African White analyses revealed that the highest

proportion of both types of biased items emerged in the Emotional Expression subscale

(table 57). More specifically, 2 non-uniform (40%) and 2 uniform (40%) biased items was

identified. In addition, from the four items that was retained in the Emotional Control

subscale, 1 (25%) exhibited non-uniform bias and 2 uniform bias (50%) (see table 59).

Over all the subscales a total of 9 items displayed non-uniform bias (18%) whilst 15 items

(31%) displayed uniform bias.


(close)

EE


(0.041; 0.11)

0.10


(0.055; 0.11)

0.028

PC Free λ20,1 17 48.79* 43.07* 23.961 - - 0.97 0.97 0.073

(0.046; 0.10)

0.077

PC Free λ14,1 16 45.78* 39.80* 7.122 - - 0.97 0.98 0.072

(0.044; 0.10)

0.093

PC Free τ14,1 15 35.47* 30.58* 18.841 - - 0.98 0.98 0.060

(0.028; 0.090)

0.27

PC Free τ20,1 14 32.64* 28.11* 7.502 - - 0.98 0.99 0.059

(0.026; 0.091)

0.29

PC Final 14 32.64* 28.11* - 4 23.76* 0.98 0.99 0.059

(0.026; 0.091)

0.29

The final PC Understanding Emotions External model (table 58) obtained NNFI, CFI, and

RMSEA values that indicate less good model fit, compared to the pattern in the other

subscales. It may be that the dimensionality of the subscale is questionable. Although the

results for the incremental fit indices in the Emotional Management Self subscale (table 58)

Table 57

MACS DIF results: Australia (n=290) and SA White (n=290) for EE

Note: EE = Emotional Expression (5 items), PC = Partially Constrained model; *p < 0.05; 1: p<0.01; 2: p<0.0125

170

obtained the 0.95 cutoff for good model fit, the RMSEA of 0.092 and p(close fit)=0.00 did not

underscore this result. The rest of the results revealed that for the remaining two subscales

(Emotional Management Others and Emotional Control; table 59) final partially

constrained models with good model fit could be specified (NNFI and CFI ≥ 0.95; p(close

fit)>0.05; RMSEA < 0.06).


(close)

UEX


(0.092; 0.10)

0.00


(0.092; 0.10)

0.00

PC Free λ23,2 377 1794.62* 1406.90* 12.443 - - 0.91 0.91 0.097

(0.092; 0.10)

0.00

PC Free λ8,2 376 1795.07* 1405.54* 12.714 - - 0.91 0.91 0.097

(0.092; 0.10)

0.00

PC Free τ29,2 375 1792.75* 1399.71* 33.343 - - 0.91 0.91 0.097

(0.092; 0.10)

0.00

PC Free τ17,2 374 1764.90* 1373.50* 28.284 - - 0.91 0.91 0.096

(0.091; 0.10)

0.00

PC Free τ45,2 373 1741.44* 1350.64* 25.305 - - 0.91 0.91 0.095

(0.090; 0.10)

0.00

PC Free τ56,2 372 1724.30* 1334.53* 21.566 - - 0.91 0.91 0.095

(0.089; 0.10)

0.00

PC Free τ8,2 371 1727.00* 1332.83* 20.127 - - 0.91 0.91 0.095

(0.089; 0.10)

0.00

PC Free τ13,2 370 1712.78* 1320.82* 12.868 - - 0.91 0.91 0.094

(0.089; 0.100)

0.00

PC Free τ5,2 369 1701.67* 1310.29* 13.909 - - 0.91 0.91 0.094

(0.088; 0.099)

0.00

PC Free τ33,2 368 1696.95* 1305.16* 9.2210 - - 0.91 0.92 0.094

(0.088; 0.099)

0.00

PC Free τ16,2 367 1693.06* 1301.38* 9.6211 - - 0.91 0.92 0.094

(0.088; 0.099)

0.00

PC Final 367 1693.06* 1301.38* - 11 263.25* 0.91 0.92 0.094

(0.088; 0.099)

0.00

EMS


(0.081; 0.12)

0.00


(0.086; 0.12)

0.00

PC Free λ54,3 39 184.49* 142.59* 31.1412 - - 0.94 0.95 0.096

(0.079; 0.11)

0.00

PC Free λ 2,3 38 171.76* 132.96* 14.8813 - - 0.94 0.95 0.093

(0.076; 0.11)

0.00

PC Free λ 64,3 37 163.63* 126.82* 11.3114 - - 0.95 0.95 0.092

(0.074; 0.11)

0.00

PC Final 37 163.63* 126.82* - 3 28.88* 0.95 0.95 0.092

(0.074; 0.11)

0.00

Table 58

MACS DIF results: Australia (n=290) and SA White (n=290) for UEX, EMS

Note: UEX = Understanding Emotions External, EMS = Emotional Management Self (5 items); PC = Partially Constrained

model; *p < 0.05; 3: p<0.0025; 4: p<0.0026; 5: p<0.0027; 6: p<0.0029; 7: p<0.0031; 8: p<0.0033; 9: p<0.0035; 10:

p<0.0038; 11: p<0.0041; 12: p<0.007; 13: p<0.008; 14: p<0.01

171


(close)

EMO


(0.044; 0.11)

0.083


(0.050; 0.10)

0.050

PC Free λ44,4 17 52.76* 44.09* 8.601 - - 0.92 0.93 0.074

(0.048; 0.10)

0.065

PC Free τ39,4 16 42.31* 34.96* 19.721 - - 0.94 0.95 0.064

(0.035; 0.093)

0.19

PC Free τ15,4 15 37.31* 30.43* 10.202 - - 0.95 0.96 0.060

(0.028; 0.090)

0.27

PC Final 15 37.31* 30.43* - 3 19.11* 0.95 0.96 0.060

(0.028; 0.090)

0.27

EC


(0.0; 0.093)

0.66


(0.042; 0.11)

0.097

PC Free λ46,5 9 24.85* 22.52* 8.892 - - 0.97 0.98 0.072

(0.035; 0.11)

0.14

PC Free τ9,5 8 14.61ns 12.95ns 19.862 - - 0.99 0.99 0.046

(0.0; 0.090)

0.50

PC Free τ46,5 7 6.90 ns 5.97 ns 15.063 - - 1.00 1.00 0.0

(0.0; 0.066)

0.87

PC Final 7 6.90 ns 5.97 ns - 3 22.32* 1.00 1.00 0.0

(0.0; 0.066)

0.87

Australia and South Africa Non-White

The Emotional Expression EFA results for the South African non-White data revealed that

item 20 (“I can describe my feelings on an issue to a colleague”) did not have a high

loading (0.394) on the emotional self expression factor. The item also cross-loaded on the

other factor (Other’s perception: 0.369). Consequently, when item 20 was included in the

fully unconstrained model the results indicated that the unidimensionality assumption was

being violated (RMSEA=0.10, p(close fit) p<0.05). Hence, item 20 was eliminated from the

analyses whereupon the unconstrained model results provided support for the

unidimensionality assumption (see table 60). Therefore, the EE analyses only included four

items from the original EE subscale (see table 60).

Table 59

MACS DIF results: Australia (n=290) and SA White (n=290) for EMO, EC

Note: EMO = Emotional Management Others, EC = Emotional Control; PC = Partially Constrained model; *p < 0.05; 1:

p<0.01; 2: p<0.0125; 3: p<0.016

172


(close)

EE


(0.0; 0.10)

0.49


(0.022; 0.092)

0.32

PC Free λ32,1 9 20.25* 17.25* 6.962 - - 0.98 0.99 0.052

(0.0077; 0.089)

0.41

PC Free λ7,1 8 12.66 ns 10.92 ns 12.0212 - - 0.99 0.99 0.033

(0.0; 0.077)

0.69

PC Free τ7,1 7 10.03 ns 8.66 ns 7.881 - - 0.99 1.00 0.027

(0.0; 0.076)

0.73

PC Final 7 10.03 ns 8.66 ns - 3 12.22* 0.99 1.00 0.027

(0.0; 0.076)

0.73

UEX


(0.069; 0.080)

0.00


(0.072; 0.082)

0.00

PC Free λ43,2 377 1415.76* 1122.50* 29.563 - - 0.93 0.93 0.077

(0.072; 0.082)

0.00

PC Free λ45,2 376 1401.72* 1108.27* 20.994 - - 0.93 0.93 0.076

(0.071; 0.081)

0.00

PC Free λ1,2 375 1394.88* 1101.79* 13.415 - - 0.93 0.93 0.076

(0.071; 0.081)

0.00

PC Free λ23,2 374 1392.92* 1100.71* 13.136 - - 0.93 0.93 0.076

(0.071; 0.081)

0.00

PC Free λ56,2 373 1390.04* 1097.71* 8.917 - - 0.93 0.93 0.076

(0.071; 0.081)

0.00

PC Free τ45,2 372 1355.72* 1069.67* 43.003 - - 0.93 0.93 0.075

(0.069; 0.080)

0.00

PC Free τ17,2 371 1331.54* 1047.40* 42.954 - - 0.93 0.94 0.074

(0.068; 0.079)

0.00

PC Free τ27,2 370 1320.82* 1037.63* 20.545 - - 0.93 0.94 0.073

(0.068; 0.079)

0.00

PC Free τ13,2 369 1307.57* 1024.54* 35.606 - - 0.94 0.94 0.073

(0.067; 0.078)

0.00

PC Free τ56,2 368 1303.12* 1019.56* 14.347 - - 0.94 0.94 0.073

(0.067; 0.078)

0.00

PC Free τ59,2 367 1295.26* 1011.73* 14.528 - - 0.94 0.94 0.072

(0.067; 0.078)

0.00

PC Free τ23,2 366 1295.87* 1010.59* 14.069 - - 0.94 0.94 0.072

(0.067; 0.078)

0.00

PC Final 366 1295.87* 1010.59* - 12 199.57* 0.94 0.94 0.072

(0.067; 0.078)

0.00

No invariant items could be identified for the Emotional Control subscale. The Emotional

Management Self subscale (see table 61) obtained the highest proportion of both types of

biased items (42.85% non-uniform and 42.85% uniform). In the Emotional Expression

subscale 2 of the 4 retained items displayed non-uniform bias (50%) and 1 uniform bias

(25%) (see table 60). The Emotional Management Others subscale (table 61) obtained the

highest proportion of uniform biased items (3 items, 60%). However, the specification that

Table 60

MACS DIF results: Australia (n=337) and SA Non-White (n=337) for EE, UEX

Note: EE = Emotional Expression (4 items), UEX = Understanding Emotions External, PC = Partially Constrained model;

*p < 0.05; 1: p<0.01; 2: p<0.0125; 3: p<0.0025; 4: p<0.0026; 5: p<0.0027; 6: p<0.0029; 7: p<0.0031; 8: p<0.0033; 9:

p<0.0035

173

at least 2 items should be found invariant was still adhered to. The final partially

constrained MACS models for three of the four subscales obtained very good model fit

(NNFI and CFI ≥ 0.95; p(close fit)>0.05; RMSEA ≤ 0.05). Consistent with the South African

White results, the Understanding Emotions External subscale did not obtain model fit

results in the same range as the other subscales. Although a slightly better RMSEA (0.072)

was obtained (than in the South African White results), the NNFI and CFI values of 0.94

and p(close fit)=0.00 once again may be interpreted as evidence that negate the

unidimensionality assumption. Over all the subscales, a total of 16 (33%) and 19 (39%)

items displayed non-uniform and uniform bias, respectively.


(close)

EMS


(0.031; 0.073)

0.39


(0.078; 0.11)

0.00

PC Free λ54,3 39 143.12* 118.43* 55.723 - - 0.96 0.96 0.078

(0.062; 0.094)

0.0024

PC Free λ64,3 38 117.56* 97.52* 31.644 - - 0.97 0.97 0.068

(0.052; 0.085)

0.036

PC Free λ2,3 37 95.40* 79.69* 33.781 - - 0.98 0.98 0.059

(0.041; 0.076)

0.20

PC Free τ60,3 36 87.85* 72.83* 16.323 - - 0.98 0.98 0.055

(0.037; 0.073)

0.30

PC Free τ54,3 35 80.30* 66.08* 13.684 - - 0.98 0.99 0.051

(0.032; 0.070)

0.43

PC Free τ64,3 34 75.72* 61.86* 11.441 - - 0.98 0.99 0.049

(0.029; 0.069)

0.49

PC Final 34 75.72* 61.86* - 6 94.93* 0.98 0.99 0.049

(0.029; 0.069)

0.49

EMO

Fully Unconstrained 10 17.59* 15.00ns - - - 0.97 0.98 0.039

(0.0; 0.076)

0.64


(0.054; 0.10)

0.030

PC Free λ15,4 17 58.29* 49.87* 11.631 - - 0.88 0.90 0.076

(0.052; 0.10)

0.039

PC Free τ15,4 16 44.65* 38.16* 25.041 - - 0.91 0.93 0.064

(0.038; 0.091)

0.17

PC Free τ39,4 15 35.11* 29.65* 15.322 - - 0.94 0.95 0.054

(0.024; 0.082)

0.37

PC Free τ58,4 14 30.96* 25.84* 8.465 - - 0.95 0.96 0.050

(0.017; 0.080)

0.46

PC Final 14 30.96* 25.84* - 4 30.12* 0.95 0.96 0.050

(0.017; 0.080)

0.46

Note: EMS = Emotional Management Self (5 items); EMO = Emotional Management Others; PC = Partially Constrained

model; *p < 0.05; 1: p<0.01; 2: p<0.0125; 3: p<0.007; 4: p<0.0083; 5: p<0.016

Table 61

MACS DIF results: Australia (n=337) and SA Non-White (n=337) for EMS, EMO

174

Australia and Sri Lanka

No invariant items in the Emotional Management Others subscale could be identified. The

Emotional Control (see table 62) subscale obtained the highest proportion of both uniform

and non-uniform biased items (both 60%). For the remaining three subscales (tables 62 and

63), at least 42.85% of the items in each scale displayed uniform bias. Overall, the highest

proportion of biased items over the five SUEIT subscales emerged in these analyses

(compared to the other cross-cultural DIF analyses presented previously). That is, 18 (36%)

of the items displayed non-uniform bias, whilst 23 items (47%) displayed uniform bias.

Although evidence for close fit was not obtained for the PC final Emotional Expression and

Understanding Emotions External models, the remaining model fit results was within the

range (NNFI, CFI ≥ 0.95, RMSEA ≤ 0.07) obtained by the other PC final MACS models.


(close)

EMS


(0.038; 0.067)

0.37


(0.053; 0.077)

0.020

PC Free λ54,3 39 153.13* 121.01* 36.971 - - 0.98 0.98 0.060

(0.048; 0.072)

0.084

PC Free λ2,3 38 139.67* 109.66* 27.342 - - 0.98 0.98 0.057

(0.044; 0.069)

0.17

PC Free τ54,3 37 123.04* 95.71* 38.401 - - 0.98 0.98 0.052

(0.039; 0.065)

0.38

PC Free τ2,3 36 110.25* 85.05* 22.782 - - 0.98 0.99 0.048

(0.035; 0.062)

0.57

PC Free τ49,3 35 106.73* 81.71* 6.383 - - 0.98 0.99 0.048

(0.034; 0.061)

0.59

PC Final 35 106.73* 81.71* - 5 67.61* 0.98 0.99 0.048

(0.034; 0.061)

0.59

EC


(0.053; 0.099)

0.033


(0.099; 0.13)

0.00

PC Free λ6,5 17 156.76* 131.61* 50.733 - - 0.87 0.89 0.11

(0.091; 0.12)

0.00

PC Free λ9,5 16 152.80* 127.98* 11.074 - - 0.87 0.89 0.11

(0.091; 0.12)

0.00

PC Free λ19,5 15 149.71* 125.18* 6.465 - - 0.87 0.89 0.11

(0.091; 0.12)

0.00

PC Free τ6,3 14 75.78* 61.41* 131.223 - - 0.93 0.95 0.076

(0.057; 0.096)

0.013

PC Free τ9,3 13 59.87* 47.85* 31.124 - - 0.95 0.97 0.068

(0.048; 0.089)

0.071

PC Free τ19,3 12 56.83* 44.66* 8.225 - - 0.95 0.97 0.068

(0.048; 0.090)

0.072

PC Final 12 56.83* 44.66* - 6 132.95* 0.95 0.97 0.068

(0.048; 0.090)

0.072

Note: EMS = Emotional Management Self (5 items); EMO = Emotional Management Others; EC = Emotional Control; PC

= Partially Constrained model; *p < 0.05; 1: p<0.007; 2: p<0.0083; 3: p<0.01; 4: p<0.0125; 5: p<0.0025; 4: p<0.0026; 5:

p<0.016

Table 62

MACS DIF results: Australia (n=587) and Sri Lanka (n=587) for EMS, EC

175


(close)

EE


(0.063; 0.11)

0.0050


(0.078; 0.11)

0.00

PC Free λ32,1 17 121.11* 108.71* 7.811 - - 0.94 0.95 0.096

(0.079; 0.11)

0.00

PC Free τ32,1 16 93.27* 82.73* 45.641 - - 0.95 0.96 0.084

(0.067; 0.10)

0.00

PC Free τ20,1 15 68.25* 59.49* 54.562 - - 0.97 0.98 0.071

(0.053; 0.091)

0.030

PC Free τ7,1 14 64.84* 55.92* 6.6812 - - 0.97 0.98 0.071

(0.052; 0.092)

0.033

PC Final 14 64.84* 55.92* - 4 60.47* 0.97 0.98 0.071

(0.052; 0.092)

0.033

UEX


(0.056; 0.064)

0.00


(0.074; 0.082)

0.00

PC Free λ17,2 377 2029.00* 1703.15* 36.63 - - 0.91 0.91 0.077

(0.074; 0.081)

0.00

PC Free λ43,2 376 2002.79* 1677.32* 39.124 - - 0.91 0.91 0.077

(0.073; 0.081)

0.00

PC Free λ34,2 375 1981.56* 1653.74* 38.065 - - 0.91 0.91 0.076

(0.073; 0.080)

0.00

PC Free λ13,2 374 1952.90* 1623.79* 36.266 - - 0.91 0.92 0.076

(0.072; 0.079)

0.00

PC Free λ48,2 373 1941.73* 1615.34* 29.567 - - 0.91 0.92 0.075

(0.072; 0.079)

0.00

PC Free λ45,2 372 1927.76* 1601.95* 22.348 - - 0.92 0.92 0.075

(0.071; 0.079)

0.00

PC Free λ29,2 371 1917.14* 1590.73* 12.179 - - 0.92 0.92 0.075

(0.071; 0.079)

0.00

PC Free τ16,2 370 1789.24* 1475.82* 157.583 - - 0.92 0.93 0.071

(0.068; 0.075)

0.00

PC Free τ34,2 369 1666.45* 1370.58* 141.944 - - 0.93 0.93 0.068

(0.064; 0.072)

0.00

PC Free τ43,2 368 1573.62* 1288.22* 151.565 - - 0.94 0.94 0.065

(0.061; 0.069)

0.00

PC Free τ17,2 367 1495.90* 1219.61* 113.966 - - 0.94 0.94 0.063

(0.059; 0.067)

0.00

PC Free τ13,2 366 1433.10* 1163.94* 100.047 - - 0.94 0.95 0.061

(0.057; 0.065)

0.00

PC Free τ29,2 365 1400.65* 1134.16* 68.188 - - 0.95 0.95 0.060

(0.056; 0.064)

0.00

PC Free τ27,2 364 1375.68* 1110.99* 48.449 - - 0.95 0.95 0.059

(0.055; 0.063)

0.00

PC Free τ22,2 363 1368.37* 1103.82* 16.3010 - - 0.95 0.95 0.059

(0.055; 0.063)

0.00

PC Free τ52,2 362 1363.88* 1099.09* 10.5011 - - 0.95 0.95 0.059

(0.055; 0.063)

0.00

PC Final 362 1363.88* 1099.09* - 16 104790.3* 0.95 0.95 0.059

(0.055; 0.063)

0.00

Table 63

MACS DIF results: Australia (n=587) and Sri Lanka (n=587) for EE, UEX

Note: EE = Emotional Expression (5 items), UEX = Understanding Emotions External, EMS = Emotional Management Self (5

items); EMO = Emotional Management Others; EC = Emotional Control; PC = Partially Constrained model; *p < 0.05; 1:

p<0.01; 2: p<0.0125; 3: p<0.0025; 4: p<0.0026; 5: p<0.0027; 6: p<0.0029; 7: p<0.0031; 8: p<0.0033; 9: p<0.0035; 10:

p<0.0038; 11: p<0.0041; p<0.007; 11: p<0.0083; 12: p<0.016

176

4.5.8.4 Discussion: MACS DIF results

As predicted, various discrepancies in the DIF results obtained with the MACS, compared

to the ANOVA approach, exist. A key question, however, was whether a similar pattern of

the number of item bias that emerged, as the CD with Australia increased, were obtained.

Hence, it was hypothesised that:





Partial41

support for hypothesis 7 emerged. The ANOVA DIF results revealed the

following pattern (from least to most bias): New Zealand (none), South Africa (none), USA

(2% uniform), South Africa Non-White (4% uniform, 4% non-uniform), Sri Lanka (14%

uniform), and Italy (32% uniform, 2% non-uniform). The MACS result pattern was as

follows: New Zealand (2% uniform, 4% non-uniform), USA (8% uniform, 12% non-

uniform), South Africa White (31% uniform, 18% non-uniform), South Africa Non-White

(39% uniform, 33% non-uniform), Italy (36% uniform, 38% non-uniform), and Sri Lanka

(47% uniform, 36% non-uniform). Although there are slight differences in these two

patterns, the MACS results, similar to the ANOVA results, revealed a marked difference in

amount of biased items between the Western (e.g. New Zealand, USA – least) and non-

Western cultures (e.g. Sri Lanka - most).

However, the proportion of MACS DIF items in each analysis was noticeably higher, than

in the ANOVA results (when similar cultures are compared). No cut-off value for the

proportion of DIF items on a scale above which practical implications may be important,

exist. Chan (2000, p.191) holds that, “when DIF occurs for only one or a few out of many

items on a scale, the practical implication may be trivial”. He advises computing an index

41

Partial support was obtained as there was a clear trend in terms of more biased items emerging in the non-

Western (e.g. Sri Lanka – larger CD) country analyses in comparison with the Western (e.g. New Zealand –

smaller CD) countries. However, the MACS results pattern confirmed neither of the CDR patterns (i.e. tables

4 and 5) completely.

177

of ‘practical significance’ by computing the mean score on the affected subscale for each

group, with and without removing the offending item(s). Then the standardised mean

difference (d statistic) is computed over the two groups42

. A summary of the results over

all the affected subscales and cultural groups are provided in table 64. Generally, the

following criterion for the interpretation of Cohen’s d, is applied: small, d = 0.2 to 0.49;

medium, d = 0.5 to 0.79; large, d = 0.8 or bigger (Cohen, 1988).

The results in table 64 indicate that over all the subscales and cultural groups, the practical

implication of removing the DIF items is small. However, relative to the other cultural

groups, the average between-group differences for Italy (average d over all subscales =

0.19) and Sri Lanka (average d over all subscales = 0.17) was the largest. This was

followed by the South African non White (average d = 0.09), the South African White

(average d = 0.07) and USA results (average d = 0.05). What is evident from the results is

that the transfer of the SUEIT to Sri Lanka, Italy or South Africa, would require a fair

number of effort spent on adaptation of the original instrument (judged by the number of

affected items) even if the sensitivity analyses (as conducted here) does not show evidence

of large influences when affected items are removed. It should also be noted that in some

cases only a minimum of two items showed no DIF (e.g. Emotional Control and Emotional

Expression subscales in Sri Lanka, Emotional Management Others and Emotional

Expression subscales in South African non-White; Emotional Control, Emotional

Management Others in South African White analyses). This would hamper the integrity of

further SEM research on these models, as a minimum of four items per construct (Muliak &

Millsap, 2000) is generally required to obtain reliable (and replicable) results.

42

For this calculation, the pooled standard deviation was used. According to Cohen (1988, p.44), “the pooled

standard deviation is found as the root mean square of the two standard deviations”.

178

Standardised mean difference

Australia &

New Zealand

Australia &

USA

Australia &

Italy

Australia &

Sri Lanka

Australia &

SA White

Australia &

SA Non-

White

EE n.a. 0.02 0.18 0.10 0.07 0.08

UEX 0.03 0.04 0.22 0.25 0.08 0.11

EMS n.a. 0.08 n.a. 0.02 0.02 0.10

EMO n.a. n.a. 0.22 n.a. 0.07 0.07

EC n.a. 0.06 0.15 0.33 0.13 n.a.

A work group of The International Test Commission (ITC) developed a set of guidelines

for the translation and adaption of psychological and educational tests for use in different

linguistic and cultural contexts (Hambleton, 1994; Van de Vijver & Hambleton, 1996).

These guidelines apply wherever tests are transported from one cultural setting to another –

regardless of whether there is a need for translation. Guidelines concerned with instrument

development and adaptation include, for example, that, “Test developers/publishers should

provide statistical evidence of the equivalence of questions for all intended populations”

(such as was provided above) and “Non-equivalent questions between versions intended for

different populations should not be used in preparing a common scale or in comparing these

populations” (Van de Vijver & Hambleton, 1996, p.95). This would imply that, based on

these results up to almost half of the items in the SUEIT should not be used to calculate

mean scores (for research or practical purposes) in the affected non-Western cultures

included in this study. Moreover, when adapting the instrument, it may be that not only

modifications in wording of items are needed. Sometimes a change in stimulus content is

also needed. It may be that non-equivalence of stimuli points to ‘cultural decentering’ that

is, “…the cross-cultural non-identity of the trait being measured by the instrument” (Berry

et al., 2002, p. 306). Hence, cross-cultural investigations based on an established taxonomic

model of EI (see Palmer et al., 2008) should be supplemented with cultural specific studies

to uncover cultural specific elements of EI.

Although the issue of partial invariance is sometimes met with skepticism it is likely to be

the typical case in many research situations (Steenkamp & Baumgartner, 1998). In this

study, partial invariance was used as a strategy where the ideal of full invariance was not

Table 64

Standardised mean difference (d statistic) for affected SUEIT subscales over various cultural groups

Note: EE = Emotional Expression; UEX = Understanding Emotions External; EMS = Emotional Management Self; EMO

= Emotional Management Others; EC = Emotional Control

179

realised, in order to allow for valid cross-cultural comparisons between the cultural groups

on the EI dimensions.

4.5.9 Results: Latent mean differences in EI

With the approach that was followed in the previous section, the final partially constrained

model provides evidence for partial metric and scalar invariance (i.e. at least two item

slopes and intercepts per subscale are found to be invariant)43

. Latent mean differences

(tabel 65) were therefore interpreted from the final partially constrained models (per EI

subscale) obtained from the MACS analyses (section 4.5.8.3).

The results revealed (table 65) that for the Australia and Italy (Emotional Management

Self), Sri Lanka (Emotional Management Others), as well as South African non-White

(Emotional Control) analyses, the minimum requirement of two invariant item slopes and

intercepts could not be satisfied. No latent mean comparisons between these groups on the

subscale scores were permissible. For the Australia and USA Emotional Management

Others’ subscale, no Kappa value was estimated as no non-zero modification indices could

be identified. Here mean scores (raw scores) may be meaningfully compared over the two

groups.

The following research questions regarding latent mean differences were investigated:

When subscale scores can be meaningfully compared across different cultures (countries),

do significant latent mean differences in emotional expression and regulation as key

aspects of emotionally intelligent behaviour exist over cultures (countries)? Are the

expected differences (i.e. significance and direction) congruent with theoretical arguments

in this regard?

43

Steenkamp and Baumgartner (1998, p.81-82) state that, “it can be shown that at least one item besides the

marker item has to have invariant factor loadings and invariant intercepts in order for cross-national

comparisons of factor means to be meaningful (proof of this proposition is available from the authors).

Ideally, a majority of factor loadings and intercepts will be invariant across countries because in that case the

latent means are estimated more reliably (i.e. they are based on many cross-nationally comparable items) and

differences in latent means succinctly summarise the pattern of differences in observed means across

countries”.

180

44KAPPA (κ)

Australia &

New Zealand

Australia &

USA

Australia &

Italy

Australia &

Sri Lanka

Australia &

SA White

Australia &

SA Non-

White

EE 0.10

(0.06)

1.67

-0.02

(0.05)

-0.43

-0.15

(0.06)

-2.64*

-0.07

(0.04)

-1.71

-0.36

(0.05)

-6.63**

-0.33

(0.06)

-5.82**

UEX 0.02

(0.04)

0.43

0.04

(0.03)

1.11

-0.34

(0.04)

-8.45**

-0.14

(0.03)

-5.15**

-0.27

(0.04)

-6.40**

-0.46

(0.04)

-10.22**

EMS 0.03

(0.05)

0.61

-0.13

(0.06)

-2.12*

n.a. -0.12

(0.04)

-2.98*

-0.26

(0.06)

-4.09**

-0.12

(0.05)

-2.23*

EMO 0.00

(0.05)

0.09

n.e. -0.64

(0.07)

-9.65**

n.a. -0.12

(0.03)

-3.72*

-0.07

(0.03)

-2.27*

EC 0.06

(0.03)

1.65

0.02

(0.04)

0.67

-0.03

(0.05)

-0.61

-0.36

(0.04)

-9.38**

-0.04

(0.07)

-0.58

n.a.

In terms of latent mean differences between Sri Lanka and Australia, it was hypothesised:


obtain a significant higher latent mean score on Emotional Control than Australia, an


Although there was a significant latent mean difference between Sri Lanka and Australia on

the Emotional Control subscale, the results revealed (tabel 65) that this difference was not

in the hypothesised direction. That is, Sri Lanka obtained a lower (not higher) latent mean

score relative to Australia, on Emotional Control. Hence, hypothesis 8 is not supported.

44

The Kappa values represent the results of the latent mean differences between each of the groups and

Australia separately. Due to the fact that individual group comparisons were conducted, it is not possible to

directly compare the different groups with each other (as the measurement scale may differ over the different

analyses). Hence no horisontal group comparisons in this table should be interpreted. Rather, each of the

values should be viewed as latent mean differences with Australia (as the reference group). As the reference

indicator value over the different subscales also differed, the magnitude of the latent mean differences

(vertical) may also not be compared with each other.

Note: EE = Emotional Expression; UEX = Understanding Emotions External; EMS = Emotional Management Self;

EMO = Emotional Management Others; EC = Emotional Control, *p < 0.05; **p < 0.001, n.a = not applicable to

calculate latent mean differences; n.e. = not estimated

Table 65

EI Latent mean differences

181


obtain a significant higher latent mean score on Emotional Management Self than

Australia, an Individualistic, low Power Distance Western society.

From the results (table 65) it is evident that a lower (not higher) mean score was estimated

for the Sri - Lanka Emotional Management Self subscale, relative to Australia. Although a

significant difference in estimated means emerged, the difference was not in the

hypothesised direction. Therefore, hypothesis 9 is not supported.


obtain a significant lower latent mean score on Emotional Expression than Australia, an


As is evident from the results (table 65), no significant difference in Emotional Expression

latent means (Sri - Lanka relative to Australia) emerged. Hence, hypothesis 10 is not

supported.

In terms of latent mean differences between Italy and Australia, it was hypothesised:


a significant lower latent mean score on Emotional Management Self than Australia, an


For the Emotional Management Self subscale in the Italian, Australian MACS analysis, the

minimum requirement of two invariant item slopes and intercepts could not be satisfied.

Hence, no latent mean for Italy (relative to Australia) on the Emotional Management Self

subscale could be estimated. Hypothesis 11 is not supported.


a significant lower latent mean score on Emotional Control than Australia, an


182

As is evident from the results (table 65) no significant difference in Emotional Control

latent means between Italy and Australia was estimated. Hypothesis 12 is not supported.


a significant higher latent mean score on Emotional Expression, than Australia, an



for the Italian Emotional Expression subscale, relative to Australia. Although a significant

difference in estimated means emerged, the difference was not in the hypothesised

direction. Therefore, hypothesis 13 is not supported.

In terms of latent mean differences between the USA, New Zealand and Australia, it was

hypothesised:


Management Self, Emotional Control and Emotional Expression subscales between the

USA and Australia, as similar Western societies.

The results revealed (table 65) that non-significant differences emerged for two (i.e.

Emotional Expression, Emotional Control) of the three hypothesised subscales in the USA /

Australia analyses. The negative kappa value for the Emotional Management self subscale

implies a lower latent mean score for the USA group, compared to Australia. Hypothesis 14

is only partially supported.


Management Self, Emotional Control and Emotional Expression subscales between New

Zealand and Australia, as similar Western societies.

As predicted, no significant differences between estimated latent means (on the three

respective subscales) for New Zealand, relative to Australia, emerged (tabel 65). Hence,

Hypothesis 15 is supported.

183

In terms of latent mean differences between South Africa White45

and Australia, it was

hypothesised:


society, will obtain a significant lower latent mean score on Emotional Expression, than


From the results (table 65) it is evident that a lower latent mean score (significant) was

estimated for the South African Emotional Expression subscale, relative to Australia.

Therefore, hypothesis 16 is supported.


society, will obtain a significant higher latent mean score on Emotional Control, than


As is evident from the results (table 65) no significant difference in Emotional Control

latent means between South Africa White and Australia was estimated. Hypothesis 17 is not

supported.


society, will obtain a significant higher latent mean score on Emotional Management Self,

than Australia, an Individualistic, moderate Uncertainty Avoidance Western society.


for the South African Emotional Management Self subscale, relative to Australia. Although

a significant difference in estimated means emerged, the difference was not in the

hypothesised direction. Therefore, hypothesis 18 is not supported.

45

No specific hypotheses were formulated for the South African non-White group, due to a moderate standing

on the uncertainty avoidance dimension.

184

4.5.10 Discussion: latent mean differences in EI

In general the results showed a clear Western (with the exception of the South African

White data) versus non-Western split in the pattern of significance of latent mean

differences. That is, with the exception of one subscale (Emotional Management Self), no

significant differences existed between New Zealand and the USA, compared to Australia.

It would, therefore, not be erroneous to conclude that equal ‘amounts’ of the respective EI

facets (measured per subscale) seem to manifest in a similar manner in these Western

groups. A similar pattern over the non-Western cultural groups (i.e. South Africa non-

White, Sri Lanka and Italy) emerged. The significant, lower latent means in almost all of

the facets of the non-Western groups (compared to Australia), indicate that these cultures

seem to exhibit ‘less’ of these EI latent traits, than their Western counterpart (i.e. Australia).

The individual non – Western group comparison results mostly did not confirm the

predicted direction of latent mean differences, based on the cultural dimension scores and

related arguments put forward in this regard. One exception is the South African (White)

Emotional Expression result. Here it was argued that the extreme low Uncertainty

Avoidance score may have the opposite effect on beliefs held regarding behaviours related

to emotional expression, than in high Uncertainty Avoidance societies (generally, more

expressive societies). That is, societies characterised by low Uncertainty Avoidance would

inhibit overly emotional expressive behavior. The result indicated that White South

Africans do seem to possess less of the emotional expression latent trait than Australians.

To the knowledge of the author, only one study that reports latent mean differences in EI

has been published. Batista-Foguet, Boyatzis, Guillen and Serlavos (2008) report on

differences in emotional and social competencies (as measured by a university specific

version of the Emotional Competency Inventory – ECI-U) over three culturally diverse

samples (from two business schools, one situated in Ohio, USA and the other in Barcelona,

Spain), after conducting the necessary invariance test. The first analysis established the

equivalence of English and Spanish versions of the test (respondents all from the Spanish

business school). In a subsequent analysis, the equivalence of English speaking US and

Asian data (US business school) with English speaking Spanish, European and Latin

185

American data (Spanish business school), were established (per competency cluster). Not

all of the competency clusters met the invariance requirements to allow latent mean

comparisons. For example, for the self-awareness46

cluster in both of the analyses, only

configural invariance was established. The authors conclude that because these

competencies refer to inner feelings, there are cultural differences in the willingness to

reveal, or express such emotional self-awareness. The Emotional Expression subscale

results in the current study may reflect a similar trend as significant latent mean differences

between Western (Australia) versus non – Western (Italy, South Africa) cultures were

found. This may mirror what is known about the differential susceptibility of cultural

influence on different components of emotion. For example, Soto, Levenson, and Ebling

(2005) found that Chinese Americans reported experiencing significant less emotion

(consistent with their cultural origin of emotional moderation) than did Mexican Americans

(origin of emotional expressiveness) when exposed to an aversive acoustical startle

stimulus. However, physiological differences among the groups were minimal and evidence

for greater emotional expression was only found when participants who most strongly

identified with their culture of origin, were compared. The authors conclude that this

reflects the extent to which emotional response (i.e. expression) are susceptible to voluntary

control as well as the social visibility of those components. Self report emotional

experiences (i.e. what we say we are feeling, how we label emotions) seem to be quite

pliable in the presence of cultural values and customs (‘display rules’).

Furthermore, in the Batista-Foguet et al., (2008) study, the Spanish group obtained higher

means in the social skills cluster (i.e. teamwork and collaboration, empathy, leveraging

diversity, building bonds) then their English counterparts (first analysis). In the second

analysis higher latent means for the self management cluster (flexibility, emotional self-

control, achievement orientation) was obtained by the US sample (Western) compared to

the English Spanish (non-Western) sample. This result is partially replicated in the current

study where higher latent means on Emotional Management Self was observed for the

Australia (Western) versus Sri Lanka, and South African groups.

46

The Self awareness cluster include a self awareness competency (i.e. recognising one’s emotions and their

effects) as well as a self-confidence competency (i.e. having a strong sense of one’s self-worth and

capabilities).

186

From the latent mean results it is clear that there are differences in EI facets possessed by

different cultural groups. These differences become more pronounced when the cultural

distance with the reference group (Australia) increases. In the absence of previous research

of this nature (with the SUEIT or other similar EI measures), these exploratory results can

only truly be accepted once replication has been shown.

4.6 General discussion

The transfer of validity (e.g. construct and predictive validity) of psychological

measurement instruments from one cultural context to another should routinely be

demonstrated. It should not be taken for granted. Larger cultural distances will generally

jeopardise the validity more (Van de Vijver & Hambleton, 1996). The notion of EI has

largely been based on Western research. Psychometric data on EI in the East, for example,

have been noted as, “rather scanty” (Gangopadhyay & Mandal, 2008, p.118) although

notable ongoing efforts do exist to adapt and evaluate Western tests for non-Western

contexts (for a review on such efforts in India, for example, see Srivastava, Sibia & Misra,

2008).

This investigation focused on the generalisability and transportability of a Western EI

measure to several Western and non-Western cultures. It aimed to examine the extent to

which culture systematically influences the transportability of the psychometric properties

of the SUEIT. Replicable factor structures, for example, may indicate the absence of

construct bias in exported Western measures. It may also strengthen beliefs in the existence

of EI as a pancultural construct.

Methodological, theoretical and practical implications of this investigation can be

identified. The most pronounced methodological implication relates to employing different

data analytic strategies for conducting cross-cultural research. For some, but not all of the

analyses, a similar pattern of results with the different methodologies (SEM versus

traditional cross-cultural methods) were attained (e.g. DIF results). The SEM results,

however, seem to be more sensitive in capturing a ‘true’ picture of subtle cultural

187

influences affecting the portability of the instrument (e.g. item keying effects). For

example, if the Tucker’s Phi results are considered one would conclude that the instrument

shows adequate structural equivalence. All, but one dimension (Emotional Management

Self in the Italian data), obtained congruence coefficients of >0.98 over all the samples.

However, the individual CFA and multi-group configural invariance results, paint a

different picture. Clear evidence emerged that substantiates the fact that there are more than

five latent traits underlying the original 64-item instrument. This replicates Gignac’s (2005)

seminal work on the SUEIT based on Australian data. However, this trend was noted in all

the other Western and non-Western results in this study.

Moreover, methodologically it was argued that the practice of conducting validity extension

studies may not be stringent enough to find the most generalisable model over various

groups. To this end it was argued that a validity generalisation procedure (Diamantopoulos

& Siguaw, 2000) should routinely be conducted. This would indicate a superior

measurement model (i.t.o. generalisation potential) between competing models. Such types

of analyses procedures may be the starting point of uncovering the universal structure of the

EI construct – a critical theoretical contribution to the development of our understanding of

the cultural nature of the construct. Facets that do not replicate could point towards cultural

specificity of the construct.

For example, Palmer et al., (2008) has recently suggested that a comprehensive taxonomy

of EI is likely to contain six facets. These include, (1) Emotional Self-Awareness, (2)

Emotional Expression, (3) Emotional Awareness of Others, (4) Emotional Reasoning, (5)

Emotional Self-Management, and (6) Emotional Management of Others. A rigorous

empirical generalisability study (with application of MI procedures) with a large multi-

national dataset (Western and non-Western cultures) may inform on the universality of

these facets. Such a study should be supplemented by a similar approach based on an

indigenous EI taxonomy (for an example see description of the Srivastava, Sibia, and

Misra, 2008, model in chapter five of this dissertation). Facets of EI that withstand a cross-

validation over Western and non-Western cultural groups of both these taxonomies, should

be regarded as cultural universal facets of the construct.

188

Regarding the transportability of the SUEIT (and the practical implications thereof), a clear

pattern over the Western cultures emerged. When the measure was scrutinised for bias (i.e.

equivalence at different levels) full measurement invariance obtained in the Australian and

New Zealand analyses support the use of the SUEIT over these two groups, without

requiring New Zealand norms. Although it is good practice to always ensure the use of

country specific norms, for research applications, however, Australian and New Zealand

respondents may be meaningfully compared on the respective latent traits (EI dimensions).

Although the results for the ‘clear’ Western countries (New Zealand and USA) was

consistent in terms of least amount of bias and no significant latent mean differences

(except for the Emotional Management Self subscale in the USA analysis) it is interesting

to note that New Zealand displayed the smallest amount of bias and not the USA. This was

contradictory to the prediction that the USA has a smaller cultural distance with Australia,

than New Zealand (see CDR patterns 1 and 2, tabels 4 and 5).

A possible explanation for this result may be the fact that there is a greater amount of

cultural integration between Australia and New Zealand, than Australia and the USA,

probably due to the geographical proximity between these two countries. Under the 1973

Trans-Tasman Travel Arrangement, citizens from Australia and New Zealand has been

allowed access (for visit, living and work purposes) to each other’s countries – without

having to apply for authority to do so47

. Australia is the largest New Zealand expatriate

community in the world. By the 2000s, it was estimated that one in nine New Zealanders

lived in Australia (Migration to Australia, n.d.). The Australian Government Department of

Immigration and Citizenship estimated 449000 New Zealand citizens present in Australia at

30 June 2005 (Fact sheet 17- New Zealanders in Australia, n.d.). Most of them are

economically active (in 2000, 62% were between 20 and 49 years) (Migration to Australia,

n.d.). The practical implication of this result is that New-Zealand emigrants working in

Australia would not be adversely affected if their SUEIT scores are used to inform selection

47

Since amendments to the Migration Act 1958 on 1 September 1994, all non-citizens lawfully in Australia,

are required to hold visas. This, however, implied no practical change in procedures for New Zealanders

entering Australia. A Special Category Visa (SCV) is automatically granted upon arrival in Australia with a

valid New Zealand Passport (Fact sheet 17- New Zealanders in Australia, n.d.).

189

decisions (in comparison with Australians). However, USA specific norms would be

required when using the instrument on native USA respondents.

Another practical implication of the Western countries’ results pertains to the ARS

response style that was evident in the data. It is not known to what extent the ARS results

may reflect a social desirability response set (i.e. the tendency to present a favourable

impression of oneself in terms of prevailing cultural norms, when responding to

questionnaire items, Mick, 1996). Research on the Bar-On EQ-i: S (Grubb & McDaniel,

2007) has shown the measure to be susceptible to social desirability. Future research /

application of the SUEIT in the Western context may benefit from the inclusion of a social

desirability measure to quantify this type of bias in the data.

Equivalence and bias are crucial issues in any society where psychological assessment tools

are used to contribute to the efficiency of selection, placement and management of human

resources in the workplace. The results of this study provide evidence that the

transportability of the SUEIT is adversely affected when the instrument is applied in non-

Western cultures with a notable cultural distance with Australia. For example, cultural

specific norms for all groups included in this study should be used to avoid adverse impact

in personnel selection decisions when applicants from different countries are being tested.

In addition, evidence emerged for a method bias effect in the data of bilingual respondents

from non-Western countries. It was shown that language proficiency influences the validity

of test results.

In South Africa, psychologists are required to be proactively48

involved in providing

evidence that tests are fair and unbiased (Van de Vijver & Rothmann, 2004). Section 8 of

the Employment Equity Act 55 of 1998 (Government Gazette, 1998) stipulates that:

“Psychological testing and other similar assessments are prohibited unless the test or the

assessment being used – (a) has been scientifically shown to be valid and reliable, (b) can

be applied fairly to all employees; and (c) is not biased against any employee or group.”

48

This is at odds with most other countries where fairness of psychological tests are assumed, unless proven

otherwise.

190

This implies that South African I/O Psychologists should be pursuing the validation of

existing instruments for use in multicultural, multilingual groups. This is a daunting task as

South Africa has 11 national languages. However, in a qualitative study on psychological

test usage in South African workplaces, two views regarding the practical implication of

non-mother tongue testing, generally emerge (Paterson & Uys, 2005). Some argue in

favour of English testing as language barriers (not being proficient in English due to

speaking another mother tongue) will reflect in work performance. Practitioners that hold

this view simply disregard the stated legal requirements for demonstrating unbiased

assessment of multilingual groups. Others do acknowledge that poor test performance may

be due to poor language skills and not the latent trait being measured (Paterson & Uys,

2005). This study provide further evidence that the latter view is a true reflection of the

reality of cross-cultural multilingual assessment in South Africa. It concurs with similar

South African studies on other individual difference attributes (see e.g. Foxcroft & Aston,

2006). In an attempt to meet legal requirements and assure ethical use of the SUEIT in

multilingual cultural groups in general (not just in South Africa), all role players (i.e. I/O

Psychologists, researchers) should be cognisant of this challenge and its implications.

For example, cross-cultural adaptation of the SUEIT may be necessary. According to Van

de Vijver and Tanzer (1997), three options should be considered. Application involves a

literal translation of the instrument into the target language. Here it is assumed that the

underlying constructs are similar in the different cultural groups. Concurrent development

of dual language assessments (e.g. Solano-Flores, Trumbull & Nelson-Barber, 2002) may

be used to overcome limitations of the Application approach. Adaptation include a

combination of development of new items (to incorporate cultural peculiar expressions), as

well as changes to/and literal translation of existing items. With the Assembly option a new

instrument is assembled. DIF of a majority of the original items or indigenous facets of the

construct, not included in the original instrument, are reasons for choosing Assembly.

Undoubtedly, the cost effectiveness of these options will differ (for a review on costs and

benefits of cross-cultural assessment for different role players, see Van de Vijver, 2002).

191

This investigation also focused on culturally driven response styles (i.e. ERS or ARS) as a

source of method bias in cross-cultural assessment. ERS differences were found to be

negligible. ARS was evident in the Western cultural groups. Overall, however, it was

concluded that these response styles may not have been a confounding method bias factor

in the current data. The theoretical contribution of the results centered on the replication of

previous research on culturally driven response styles. The results mostly did not confirm

previous research. However, closer inspection of sample properties revealed that the some

of the results could be explained in terms of social mobility theory (i.e. movement from a

low to higher status group) and the cultural accommodation hypothesis (see section 4.5.7).

That is, the effects of changes in education and socio economic standing (associated with

social mobility theory), as well as bilingualism on respondents’ survey responses.

Two implications of these results for future research should be mentioned. Firstly, it is

imperative that future studies collect information on subject and context variables (e.g.

educational background, socio economic status, attitudes regarding cultural origin). This

would allow for an empirical verification of social mobility in a given cultural group (e.g.

non-White South Africans). It may inform on the need to establish specific group norms for

such ‘sub-cultural’ groups in the workplace. Secondly, social mobility may be associated

with a greater measure of psychological acculturation. Psychological acculturation refers to

changes that an individual experiences as a result of being in contact with other cultures

(Berry et al., 2002). As the speed of globalisation accelerates, the workplace continues to

see unprecedented collaborations from people with diverse backgrounds and cultures. For

example, one in every 10 persons living in more developed regions is a migrant, whilst

more than 21% of the Australian and 17% of the Canadian population, are foreign born

(Hong, Wan, No, & Chiu, 2007). According to Ota (2004) exposure to knowledge from

different cultures highlights cultural contrasts and could trigger cultural change. Increased

global communication may cause shifts in cultural values (e.g. Collectivism to

Individualism). China, for example, has undergone major economic and social

transformation. Here a movement away from traditional collectivistic values is evident in

modernity and individualistic values portrayed in current Chinese advertising (Zhang &

Shavitt, 2003). On an individual level, acculturation results in, “the multidimensionality of

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cultural selves” (Hong et al., 2007, p.329). That is, multiple cultural identities that coexist

in any given individual without adverse psychological consequences. With prolonged

contact, individuals may develop adjustment repertoires that facilitate internalisation of

new cultural identities. Such changes could help overcome cultural difficulties encountered

in multicultural workplaces, due to cultural differences. Hence, the effects of psychological

acculturation should be considered in research and practical applications of cross-cultural

assessment.

One such effect is linguistic assimilation. It entails a movement from a subordinate

ethnolinguistic group (i.e. Sinhala, Afrikaans) to a dominant ethnolinguistic group

(English). Such language shifts may also result in cultural value shifts (Van der Merwe,

Ashley, Charton, & Huber, 1974). Future research should routinely explore the effects of

bilingual responses in cross-cultural assessment. For example, Muller, Hausmann, and

Straatmann (2008) recently explored the effects of bilingual responses on the MI of a job

satisfaction measure (n=11500 from 10 countries). The results revealed substantial

commonalities in the degree of cross-cultural MI for both groups (native English

respondents versus bilinguals’ responses to the English measure). Adopting such a research

design allows for the testing of specific response characteristics of bilinguals or other

sources of measurement equivalence, independent of translation ambiguities. The results of

such studies should be used to decide whether translation equivalent versions of the

instrument are needed for unbiased testing in a multilingual environment.

In the final part of this investigation an exploratory investigation of latent mean differences

on the EI facets included in the SUEIT, was conducted. No previous research of this nature

could guide hypotheses in this regard. It was argued that the most prominent cultural

dimension characteristics of a respective cultural group (e.g. Collectivism) would find

expression in prominent differences in EI facets (e.g. emotional expression) in order to

conform with cultural norms of emotionally intelligent behavior for that group (i.e.

primarily Western versus non-Western). Almost none of the predictions were supported by

empirical results. It should be noted that the partial invariance models should be considered

as weak models of the measured construct. This is because in most cases only two items

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were retained upon which latent mean differences could be estimated. The use of two items

(to measure one latent trait) could be considered as a severe construct under representation

and for practical purposes would not be deemed suitable to measure a psychological

construct appropriately. Thus, although this procedure allowed for a preliminary

exploratory look at the latent mean differences between the cultural groups, replication is

needed.

4.7 Limitations and suggestions for future research

Several limitations of this study should be noted. This study aimed to investigate whether

Hofstede’s cultural framework could be used to systematically account for the pattern of

transportability of the SUEIT to other Western and non-Western cultures. Generally, a

Western / non-Western pattern in transportability emerged in most of the results. Finer

distinctions between Western and non-Western cultures, as evidenced by the Cultural

Distance (Kogut & Singh, 1988) calculation and CDR pattern, was not confirmed. There

was also a lack of consistency over the results. That is, different patterns of transportability

emerged for the configural and metric invariance results. None of these confirmed either of

the CDR patterns (tables 4 and 5) calculated for this study. The method bias results

provided evidence that a common method artifact may have obscured the invariance results

of the South African data, affecting the resultant transportability pattern. This being said, it

makes sense to discuss the limitations inherent to, firstly, the nature of the Hofstede cultural

dimensions and, secondly, with the use of cultural dimensions in the construction of a CD

index, as was utilised in this study. Then several methodological limitations of this study

will be addressed. Suggestions for future research are incorporated into the discussion

where applicable.

Hofstede’s framework of cultural dimensions has been very prominent and its impact

widespread. Some have argued that it has continuing authority in various disciplines;

including international business research (Chandy & Williams, 1994) as well as psychology

(Oyserman, Coon, & Kemmelmeier, 2005; Triandis, 1994). At the time (1980s), Culture’s

Consequences promoted sensitivity to cultural diversity in the workplace and beyond

(Ailon, 2008), illustrating that national culture could not be disregarded in an increasingly

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global environment. Since its inception, criticism has been severe (e.g. Kitayama, 2002;

Schooler, 1983; Singh, 1990). Ongoing points of criticism relate to the internal validity of

the dimensions, as well as how they were constructed (Kogut & Singh, 1988). Hofstede

(2001) himself notes most common sources of criticism and his response to them in the

latest edition of Cultures Consequences (Hofstede, 2001). These range from issues related

to using nations as units for culture, the questionable number of dimensions and out-

datedness of the data. However, more recently Blodgett, Bakir and Rose (2008) argued that

the conceptual appeal of the framework tends to blind researchers to the fact that little

empirical scrutiny of the psychometric properties of the Values Survey Model instrument

has been noted. This echoes McSweeney’s (2002) view that ongoing absolute acceptance of

the framework suggests a far too loose standard of ‘acceptable evidence’ (i.e. empirical

research evidence) in some management disciplines. For example, Kagitcibasi (1994) has

reported low reliabilities of the dimensions. Little congruency between Hofstede’s and

Schwartz’s49

(1994) cultural frameworks has been found (Ng, Lee, & Soutar, 2007). In

addition, a very recent study (Blodgett et al., 2008) found that a majority of the items in

Hofstede’s cultural instrument did not exhibit face validity. Moreover, the reliabilities of

the four dimensions were low (i.e. Individualism / Collectivism, α=0.66; Masculinity /

Femininity, α=0.65; Uncertainty Avoidance, α=0.35; Power Distance, α=0.30) and no

evidence for the four factor structure emerged. The authors caution against using the

cultural framework at an individual unit of analysis level. National culture indices utilise a

high level of aggregation, which could hide important variations in individual differences

and experiences (Laroche, Kalamas, & Cleveland, 2005). Ailon (2008), however, recently

argued that another more fundamental challenge related to using Hofstede’s indices is

embedded in the cultural relativism50

constituting the essence of the writing of Culture’s

Consequences. He provides a thought provoking analysis, ‘mirroring’ the work against

itself in terms of its own proposed value dimensions (Power Distance, Individualism etc.),

49

Schwartz (1994) identified ten individual-level value dimensions from data spanning 41 cultural groups in

38 (later 64) nations. His approach spans both individual and national levels of analyses. There are conceptual

similarities with Hofstede’s dimensions (Hofstede, 2001) although Schwartz (1994) has argued that his

dimensions are more grounded in the literature and empirically validated. 50

Ailon (2008) holds that Hofstede’s commitment to cultural relativism developed and tightened throughout

his career as evidenced by, amongst others, his understanding of the Western bias inherent in his work in the

late 1980s (Hofstede & Bond, 1988; Hofstede 1991), and assertions of the cultural boundedness of 1990s

American and European management writings (Hofstede 1994, 1996)

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to answer the question: in what ways, and to what effect is the book itself bounded by the

cultural milieu from which it sprung? His analysis illustrates how the text (similar to other

mainstream organisational texts) constructs the social reality in ways that attribute lesser

value to various others. He concludes that (Ailon, 2008, p.900),

“It is a problem of power – of a tendency to see the world in a way that is not only limited by one’s

own culture but also limiting in terms of its effect on other cultures. Apparently, the cultural bias is not

politically random. Theorising this bias should take into account the fact that it is characterised by the

tendency to suppress foreign cultures - to subject them to categories of analysis that make them familiar in

ways that privilege ‘our’ own…when their findings are applied by practitioners, they run the risk of creating a

vicious cycle in which organisational members are required to define their indigenous culture in terms that are

external and foreign to it and, moreover, that essentialise its inferiority. The devaluing / overvaluing

mechanisms may thus be continually reproduced. Striving to influence, and in fact influencing actual work

lives, writers should be especially watchful of the political subtext of their research reports – of their

‘science’”.

What is the implication of this for future cross-cultural EI research? Future research should

seek to replicate the current study with other cultural frameworks. Two such frameworks,

generally understood as further developments of Hofstede’s work, exist: the work of

Schwartz (1994) and the GLOBE (House et al., 2004) study (briefly mentioned in chapter 2

of this dissertation). Replications of similar trends in latent mean differences (as was found

in this study) on EI facets may inform on such universal cultural differences. To guard

against the cultural boundedness of further EI research, the ‘integrated epistemology’ for

conducting research on EI in the workplace, suggested by Emmerling (2008), should be

followed. Emmerling (2008) argues that the zeitgeist of current EI research may be limiting

our ability to produce research insights that inform practice related to developing EI in

diverse cultures. He calls for (Emmerling, 2008, p.81), “…the current desire to generalise

theory and practice be tempered with a desire to establish the validity and utility of specific

EI-based theories within specific cultures and organisations.”

In addition to the criticism of the Hofstede framework, the concept of cultural distance

(derived from the Hofstede indices) that was utilised in this study has also been under

scrutiny for various conceptual and methodological reasons (discussed in section 4.2). Most

pronounced, and of particular interest to this study, is that it has been argued that

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Hofstede’s dimensions fail to capture the complexities of national culture (e.g. Shenkar,

2001). In addition, the robustness of the Kogut and Singh (1988) method for constructing a

CD index has also been scrutinised. Failure to interpret the results of this study in terms of

the CDR patterns may thus be a consequence of the aggregated nature of the national

cultural dimensions as reflected in the CD index. In addition, for Sri Lanka and the South

African non-White data no Hofstede county scores were available. For Sri Lanka, an

aggregated score derived from other countries in the Southern Asian cluster were used.

However, there may be substantial differences between such ‘close’ countries (Harzing,

2003). Hence, the results of this study should be interpreted within the boundaries of this

limitation of the CD construct. Future research should seek to replicate the current study

with other cultural frameworks mentioned previously.

A last point of criticism against the use of Hofstede indices, relates to what Hofstede

termed the ‘ecological fallacy’ (Hofstede, 2001). Originally coined by Robinson (1950), it

points towards the fact that it is often inadequate to generalise a relationship which was

found on an aggregate level to the individual level. That is, national level data of cultural

frameworks are used to predict individual or organisational behaviour despite no guarantee

that an individual or organisation will act in the way his/her national culture dictates (Kim

& Gray, 2008). This is also a limitation of the current study.

Numerous methodological limitations of this study can be identified. Firstly, convenience

sampling was used. This has led to an overrepresentation of affluent countries in the final

dataset. This is often the case in cross-cultural research (Van de Vijver & Leung, 1997).

Future research should strive to include data from countries from a wider range of cultural

clusters (e.g. Europe – Latin, Nordic, Eastern; Latin America, Asia). To address research

questions related to the equivalence (e.g. linguistic etc.) of the SUEIT in South Africa, data

from at least the four biggest linguistic / ethnic groups (e.g. Xhosa, Zulu, Sepedi &

Afrikaans) should be included in subsequent studies. Secondly, although the samples were

matched in terms of age and gender, they cannot be considered representative of the

populations of the countries included in this study. This limits the generalisability of the

results. Thirdly, small samples in SEM research may result in unstable results. This is

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especially true when the measurement model is relatively complex (Hair et al, 2006) – as

was the case in this study. However, opinions regarding minimum sample sizes for SEM

research have varied (MacCallum, 2003; MacCallum, Widaman, Preacher, & Hong).

Future research on the SUEIT should ensure to employ ‘bigger’ sample sizes judged on

factors like model complexity and measurement model characteristics (see Hair et al., 2006

for a list of factors).

Cross validation of modified models (i.e. partial invariance research, as is the case with the

DIF analyses) is rarely done in practice, although repeatedly stressed in the literature

(Steenkamp & Baumgartner, 1998). A fourth limitation of this study is that the samples

were not big enough to re-estimate the various models in a validation sample (split sample).

Future research should incorporate such cross-validation procedures. Lastly, another

potential problem associated with the practice of testing for partial invariance, is related to

the selection of the reference indicator in the model. One item is selected as a referent

indicator to assign a metric to the latent variable. If however, an ‘offending’ item (i.e. the

item was calibrated to the underlying true score differently in one or more groups) is

unknowingly selected, poor fit may be an artifact of the standardisation of the latent

variable to difference metrics and not due to ‘real’ differences in the models (Chan, 2000;

Vandenberg & Lance, 2000). However, no clear solution for problem exists. Chan (2000)

recommends the testing of results with different choices in reference indicators, to see

whether the results differ substantially.

4.8 Conclusion

The aim of this research was to investigate the generalisability and transportability of the

self-report EI measure, the SUEIT (Palmer & Stough, 2001) over various cross-national

samples. Various hypotheses regarding construct, method and item bias were investigated.

Overall it would seem that the transportability of the instrument is not severely affected

when used in other Western cultures. Sufficient evidence was obtained that the EI

construct, as operationalised by the SUEIT, is generalisable to the Western countries

included in this study. No significant latent mean differences on the various EI facets, when

these countries were compared to Australia, were found. Evidence was found for cultural

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bias when the instrument was applied to respondents from several non-Western cultures.

For these cultural groups the transportability of the instrument seems to be adversely

affected. There was not sufficient evidence to suggest that larger cultural distances with

Australia, would necessary result in larger adverse effects. It may be necessary to adapt the

instrument for adequate use in some of the non-Western cultural groups.

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CHAPTER 5

Final discussion and conclusions

5.1 Introduction

Measurement has always been, and continues to be, a pivotal issue in I/O psychology

research. Numerous journals are fully, or partially devoted to measurement issues (e.g.

Applied Psychological Measurement, Journal of Applied Psychology, Multivariate

Behavioural Research, Organizational Research Methods, Structural Equation Modelling).

Developments in the evaluation of measurement quality, which extend beyond tradition

classical test theory, are in part due to recent advances in analytic tools (Vandenberg &

Lance, 2000). On a methodological level, this dissertation aimed to highlight the

importance of conducting invariance research, specifically at this fairly early stage in the

existence of the EI construct. It illustrated how SEM multi-group CFA procedures could be

utilised, within a cross-cultural research paradigm to explicate cultural bias from true

construct variance. The issue of instrument invariance is often overlooked (Steenkamp &

Baumgartner, 1998) although the implications may be severe or even fatal1 (e.g. research

results are misleading, Steenkamp & Baumgartner, 1998; weakens the conclusions of a

study, Horn, 1991). The lack of invariance studies is attributed to various factors, e.g. lack

of agreed-upon terminology, methodological complexities, and few clear guidelines for

adequate invariance (Lubke & Muthen, 2004; Steenkamp & Baumgartner, 1998;

Vandenberg & Lance, 2000). A recent narrowing towards a uniform understanding of, and

approach to invariance research is evident, based on the efforts of many researchers (e.g

Byrne & Watkins, 2003; Cheung & Rensvold, 2000; Mavondo, Gabbott, & Tsarenko,

2003; Steenkamp & Baumgartner, 1998; Vandenberg & Lance, 2000; Vandenberg, 2002)

even though many disparities still exist. Hence, some may not agree with the analytic

approach followed here although it is in accordance with acceptable standards that have

been used previously (described in the MI review by Vandenberg & Lance, 2000, and

Chan’s, 2000, article). On a pragmatic level true meaningful (cultural) group comparisons

1 For an example discussion of misleading results due to an absence of invariance evidence, refer to

Steenkamp and Baumgartner (1998, p. 88).

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regarding the nature of the EI construct would not be permissible in the absence of

invariance evidence. This may impede our understanding of the true nature of the construct

in different groups, as well as conclusions regarding how this group difference may affect

organisational functioning.

As a first step in the advancement of cross-cultural EI research, this study aimed to

illustrate the need to differentiate cultural bias from true construct variance in a self-report

mixed model measure of EI. This is important in cross-cultural EI research in general. It

also has practical implications when such measures are utilised in the increasingly

multicultural workplace. The equivalence of measurement operations of transported

measures should routinely be inspected before mean differences on the latent trait may be

meaningfully compared across groups. EI has been described as, “highly influential and

important in occupational settings, a construct that may even hold the promise of a

predictor with reduced adverse impact” (Zeidner et al., 2004, p. 394). EI has been found

predictive of real life criteria (Van Rooy & Viswesvaren, 2004) and is increasingly being

used in the workplace as a predictor (Van Rooy et al., 2005). If equivalence assumptions

remain untested, the practical utility of EI as a valid predictor when utilised over different

cultural groups, may be questionable. An absence of metric equivalence, for example,

requires within group norms to avoid adverse impact in personnel selection decisions.

Whether such norms are available and being used is an issue beyond the scope of this

dissertation. What should be noted is an increased awareness of issues regarding global

norming (i.e. the challenge of international norms). For example, a recent special issue of

the International Journal of Testing (volume 8, nr. 4) was completely devoted to aspects

related to the international norming of personality measures. Most of the concerns

addressed are also directly applicable to EI measurement. For example, applicability of

norms should not be assumed, but actively be demonstrated. In the case where a respondent

is from a different cultural group than the normative sample, person-fit statistics (see e.g.

Emons, Sijtsma, & Meijer, 2005) may be used to empirically establish the applicability of

norms.

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5.2 Theoretical and practical implications

The main contributions of this study are of a theoretical and practical nature. The evidence

for cultural bias that was found raises questions regarding the practical implications of

exporting the SUEIT from its Western origin to the other non-Western cultures included in

this study. Test transportability is an important issue in psychological measurement as

foreign developed tests are used in most countries more frequently that nationally

developed tests (e.g. Oakland, 2004). The multicultural nature of populations has, in the last

decade, become more prominent in many countries (Van De Vijver, & Rothmann, 2004).

This implies that in the global workplace the cultural heterogeneity in assessment continues

to increase, because of the composition of the applicant pool (Van de Vijver, 2008).

Equitable and fair test usage requires a keen focus on cross-cultural applicability of tests

(Paterson & Uys, 2005). Validity transfer (construct or predictive) should actively be

demonstrated.

On a practical level cross-cultural EI assessment practices should be uniformly applied –

especially when research is being conducted. As illustrated in this study, a respondent’s test

language proficiency may influence test results in the form of method bias and confound

MI (equivalence) results. Individuals with less verbal skill (e.g. bilinguals) may have

difficulty reading negatively keyed items, accurately, particularly those items with double

negatives. A possible strategy to combat this problem is that mother tongue testing should

be considered. This implies that translation equivalent versions of instruments should be

used. This applies to the cross-national application of a given instrument. It also applies to

using the instrument on subgroups or subpopulations (e.g. language groups) within a given

country (e.g. different language groups in South Africa).

It is well known that test translation poses its own problems. For example, it may not

always be possible to preserve trans-linguistic and trans-cultural meaning of items, e.g. see

Daouk, Rust, & McDowell). Constructs may have different meanings across cultures. It

may also be that items may sound stronger or weaker in the target language. This could

result in a shift in the relationship between raw scores and latent trait level (Bartram, 2008).

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Hence, the accuracy of translations should be judged in terms of linguistic and

psychological equivalence. The former implies the mapping of linguistic word and sentence

meaning. The latter entails the mapping of the psychological meaning of the item. For

example, does the item serve the same psychological function in all languages? To this

end, Harkness and Van de Vijver (2009) have recently identified four equivalence

perspectives that good test translation / adaptation should combine. These include

construct- (i.e. similarity in source and target culture), cultural- (similarity in norms about

interaction / modes of address), linguistic- (translation accuracy, i.e. the retention of

denotation and connotation of item wording), and measurement equivalence (similarity of

factors measure by the test and comparability of scores). Ensuring a high combination of all

of these constitutes good translation / adaptation practice. Due attention should also be

given to the International Test Commission’s (ITC) guidelines for test translation and

adaptation (Hambleton, 2001; Van de Vijver & Hambleton, 1996). More specifically,

guidelines in the ‘context and test development’ categories should routinely be applied

when different language versions of instruments are developed. The context category

describes issues regarding construct equivalence in the language and cultural groups of

interest. For example, guideline C1 (“Effects of cultural differences which are not relevant

or important to the main purposes of the study should be minimised to the extent possible”)

taps into the basic principle of avoiding construct, method and item bias (Van de Vijver &

Hambleton, 1996). Guidelines in the test development category cover issues like providing

evidence that language use in items are appropriate for all cultural and language

populations (D2), that test adaptation takes full account of linguistic and cultural

differences among populations (D1), and that test developers / publishers should provide

evidence of the equivalence (e.g. item equivalence, guideline D6) of different language

versions (guidelines D5, D7, D9 etc.).

Prominent EI assessments are increasingly being translated. Recently it has been reported

(Caruso, 2008) that the MSCEIT, for example, has already been, or is currently being

translated into 17 languages. Twenty two different language translations of the Bar-On EQ-

i (1997) exist (Bar-On, 2000) and 18 language translations of the TAS-20 (Parker et al.,

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2003) have been used in cross-cultural assessment. Little is known about the translation /

adaptation processes for these different versions of prominent EI measures. The ITC

guidelines specifically require that documentation of changes (when tests are translated or

adapted) should be provided2. Providing this information to potential test users is a standard

ethical requirement to be met. However, a wealth of information on cultural, linguistic, and

socioeconomic differences between the original, mostly Western cultural contexts, and

target (mostly non-Western) cultural contexts, where adaptation has been conducted, has

probably been accumulated for these tests. Consolidating this information would provide

valuable information for the future cross-cultural adaptation / translation of EI inventories.

It could also provide a unique opportunity to better understand the conceptualisations of EI

from within the cultural group to which the translation was conducted. For example, Caruso

(2008) recently provided an overview of interesting issues that were encountered when the

MSCEIT was being adapted and translated for use in Japan. In the Facilitation task of the

Using Emotions branch of the MSCEIT model, cultural differences were evident in a item

that referred to using of different moods that assist in “planning a fun birthday party at

home”. Japanese are not accustomed to having parties at their homes, and the item was

therefore adapted to suit their cultural customs. Adaptations to stories (i.e. hypothetical

situations involving a personal emotional situation) contained in the Emotional

Management Task was also necessary to contextualise the items within Japanese culture.

A further unique challenge EI researchers and test developers should be cognisant of when

cross-cultural adaptation is being conducted, is the specific role of language in emotion.

Psychological reality is interpreted within a cultural context – according to a relativistic

perspective. In this cultural or social construction, language fulfils a central role. We

interpret our environment within the frames of reference that are formed by language.

Emotions are encoded in language. For example, lexical theorists (e.g. Lutz & Abu-

Lughod, 1990) argue that the essence of emotion lies in the ways that people label their

subjective experiences. Hence, the meaning of emotion words would be particularly

2 Guideline I1 state that (Hambleton, 2001, p.169), “When a test is adapted for use in another population,

documentation of the changes should be provided, along with evidence of equivalence”.

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sensitive to the impact of culture on emotion. Wierzbicka (1986, p. 584) have persistently

argued against the notion of universal emotions, “neatly identified by means of English

words….Polish, for example, does not have a word corresponding exactly to the English

word disgust”. Other examples include that in Tahitian language there are 46 words for

anger (Levy, 1973) and no word for sadness. There is no word for depression in Xhosa

(South Africa), Chewong (Malaysia), Yoroba (Nigeria), Mandarin (Russell, 1991). Cheng

(1977) report that no exact translation in Chinese for anxiety could be found. A word for

guilt is missing from the Sri Lanka Sinhala language (Obeyesekere, 1981). Many more

examples exist. Culture also seems to dictate whether emotions are ‘hyper-cognised’

(reflected in many lexical labels) or ‘hypo-cognised’ (reflected in few lexical labels).

Meaning differences in emotion terms, differences in lexical variations, or absence of

equivalent emotion concepts will generate bias and inequivalence when Western tests are

translated / adapted for non-Western cultures. Therefore, any cross-cultural EI assessment

instrument should be developed / adapted with a thorough understanding of cross-cultural

similarities and differences in emotion terms. To this end, a recent international project on

the identification of the emotion lexicon (i.e. the prototypicality, frequency and clearness of

emotion words) and emotional referents of emotion words (on the basis of the componential

emotion theory3) in different cultural and language groups could be very useful.

Researchers at the Swiss Centre of Affective Studies have been working with a

multidisciplinary group of emotion researchers from different cultures on the componential

GRID research program (see e.g. Fontaine, Scherer, Roesch, Ellsworth, 2007). A local

South African project is also underway in the eleven national languages. One of the goals

of the program is that extensive component profiles for emotion terms in the studied

cultural groups would be documented. This would improve the precision and efficiency of

the intercultural translation of emotion words. Utilising the outcomes of this research

program (it is hoped that a consolidated dictionary of emotion terms in different cultural

3 According to this theory, emotions are not conceptualised as states but as dynamic multicomponential

processes that are triggered by specific situational antecedents. To study an emotion, then, is to study the

situational antecedents and the synchronised activity it elicits in each of the emotion components. These

include the appraisal, action tendency, subjective feelings, expression, and regulation components (Scherer,

1984).

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and languages groups would be produced) could be invaluable in the cross-cultural

translation / adaptation of current EI measures. It will also benefit the development of new

EI measures.

In this study method bias was also investigated for theoretical and practical purposes. There

is a large body of research which suggests that national differences in response styles (i.e.

culturally driven response styles) exist. Demonstrating that a measure is free of ERS and

ARS eliminates alternative explanations for observed cross-cultural differences. Such

response styles may lead to invalid inferences in cross-cultural research (Van Herk et al.,

2004) if left undetected. The results of this study were limited in replicating previous

research on response styles (i.e. theoretical contribution). The ERS and ARS results did

allow for ruling out (to a certain extent) these sources of method bias. Hence, more

confidence could be placed in the subsequent method bias results (language proficiency).

On a practical level EI researchers should be cognisant of culturally driven response styles

and that it should routinely be inspected in future cross-cultural EI research. Hanges and

Dickson (2004) propose correcting for response bias by ipsatising scores within each

individual.

This study also examined DIF of SUEIT items over various cultural groups. In chapter

three propositions regarding differences in national value dimensions and implications for

DIF was proposed. It was argued that where a larger cultural distance with Australia exists

more item bias would be detected. Overall, the pattern of results confirmed this notion. DIF

analyses, however, indicate differential functioning, but cannot explain the cause of that

difference. Studies that predict DIF is scarce. Only one other study has used Hofstede

cultural dimensions to predict the presence of DIF in a global employee opinion survey

(Ryan et al., 2000). Only seven items in one subscale (supervision) of the instrument were

investigated. That is because studies of this kind can be quite laborious since item by item

analyses are conducted to examine DIF. Similar to this study, the authors did find evidence

of higher rates of DIF among countries which are more culturally dissimilar. The authors

conclude that (Ryan et al., 2000, p.554), “…one can anticipate DIF in survey development

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by examining item content in light of cross-cultural theories. We do not think that one can a

priori determine and eliminate all DIF that will occur. However, we do believe that

awareness of cultural differences can be heightened by the use of such data in corporate

surveying.” The current study was a first step in that direction for cross-cultural EI

assessments that are being used for global corporate assessment purposes.

The results of this study also concur with the results of Ryan et al., (2000) which detected

DIF, assumed to not be ‘true DIF’ ascribed to cultural influences. For example, in this

study 10 SUEIT items were identified for which item bias were theoretically predicted

(table 3, chapter 3). The results showed that 4 (of the 10) items showed DIF in three or

more cultural groups. Item 19 “I find it easy to control my anger at work” displayed both

uniform and non-uniform DIF in three of the four non-Western samples. Uniform DIF in

three samples (Italy, South African non-White and Sri Lanka) and non-uniform DIF in

South-African non-White and Sri Lanka was evident for Item 7, “I find it hard to convey

my anxiety to colleagues”. It is interesting to note that these two items contain the words

‘anxiety’ and ‘anger’. It may well be that elements of Power Distance (South African non-

White) or Collectivism (Sri Lanka) could have ‘caused’ this DIF. This cannot be verified

by the results of this study. There is no way to establish for certain if the detected DIF is

due to cultural differences or other factors. For example, 2 other items (of the original 10),

item 2 (“I generate positive moods and emotions within myself to get over being frustrated

at work”) and 64 (“When a colleague upsets me at work, I think through what the person

has said and find a solution to the problem”) attained non-uniform DIF in three or more of

the non-Western samples. Both these items are lengthy and complex. Moreover, upon

inspection of all the DIF results, it was evident that another lengthy item (item 56, “I can

tell when a colleague feels the same way as myself about another colleague without

actually discussing it”) also produced uniform DIF in three non-Western samples. Evidence

of non-uniform DIF for this item was also found in two of these samples (Italy and South

Africa non-White). According to Budgell et al., (1995) DIF may be influenced by

numerous factors, one of which is item complexity. This underscores the previous

conclusion – DIF may be caused by various factors. Another example is found in item 54

207

(“I can easily snap out of feeling down at work”). This item showed non-uniform DIF in all

four non-Western samples. Evidence for uniform DIF in three of the non-Western samples

(i.e. Italy, South African non-White and Sri Lanka) also emerged. It may be that the

phrasing, ‘snap out of’ in this item could be a nuisance factor which represents typical

English Western jargon, more easily understood by Western than non-Western respondents.

The results do, however, raise awareness of susceptibility of items that contain emotion

words (such as items 19 and 7) that might have different meaning connotations over

different cultural groups. It also points toward other item characteristics (e.g. ambiguities in

interpretation, low familiarity / appropriateness of item content; Van de Vijver & Tanzer,

2004) that may influence DIF. Such characteristics should also be taken into account in EI

test development / adaptation. For example, in future cross-cultural adaptations of the

SUEIT item complexity should be reduced. Lengthy items (e.g. 2, 56 and 64) should be

shortened. In addition, when items that contain specific emotion words (e.g. anger, anxiety)

are translated, care should be taken that correct translations are used.

Lastly, this dissertation contributed to our understanding of whether cultures differ in

various emotional intelligence facets. The limitations of the partial invariance models upon

which the latent mean differences were estimated, impede the practical implications of the

results. Future research should aim to replicate the current results. It may well be that the EI

construct and its facets is more salient in Western, affluent countries. Van de Vijver and

Poortinga (2002), for example, observed this trend with the postmaterialism scale

(emphasising self-expression and quality of life) in the World Values Survey 1990 – 1991

of Inglehart (1997). This construct (postmaterialism) was easier to measure and more

relevant in affluent countries. Future cross-cultural EI research may provide an answer to

the question if a similar trend could be noted for the EI construct.

5.3 Future research

What can be done to deal with bias? To deal with construct bias, a strategy of cultural

decentering (Werner & Campbell, 1970) can be used. This implies removing cultural

particulars and restricting the instrument to universal aspects found across the cultures.

208

Another strategy is to examine cross-cultural differences in the nomological network of EI.

It could point to the presence of construct bias in the Western current conceptualisation of

the construct, which limits its generalisability potential to non-Western cultures. A

convergence approach as specified by Campbell (1986) may also be applied. That is,

independent within-culture developments of instruments, with subsequent cross-cultural

administration of all instruments, are conducted.

The development of EI inventories in Asia and Africa (convergence approach), independent

of Western influences, might add valuable knowledge to the current Western

conceptualisation of the construct. The development of such indigenous scales may uncover

other aspects of emotional intelligent behaviours which are cultural specific, and have

strong predictive validity within that culture (or even other cultures). It is proposed that the

development of such inventories should be completely void of Western influence so as to

allow for true cultural conceptualisations of the construct to be captured. For example, even

though The Emotional Intelligence Scale (WLEIS) was developed in Hong Kong by Wong

and Law (2002) it may still not capture the full conceptualisation of EI in this culture. This

is because the scale was developed by asking students to generate items for the four Mayer

and Salovey (1997) EI dimensions. It may be argued that this imposed a Western,

ethnocentric definition of the construct into the scale development process. Although a

recent replication in the Beijing and Shandong provinces in mainland China found support

for the four factor structure of the WLEIS (Wang, 2007), it does not necessarily preclude

the existence of other dimensions of EI in this culture (not included in this scale).

Sibia, Srivastava, and Misra (2003) recently developed an Indian indigenous model of EI.

The model has four dimensions which makes explicit the fact that EI is embedded in a

traditional, religious and philosophical context. Rooted in a strong collectivistic value

orientation, it acknowledges the role of family and society in the shaping of emotions. For

example, the first dimension social sensitivity, encompass all aspects of relating to others.

That is, showing respect and engaging in pro-social activities (e.g. helping, cooperating,

empathizing), expressing and experiencing affection (including understanding other’s

209

emotions and connect with them), as well as building social support for oneself by

influencing others and appropriately express and control of negative emotions (e.g. anger,

unhappiness, intolerance). The second dimension, pro-social values, focuses on enhancing

group welfare through values like patience, affect, kindness and endurance. Action

tendencies, the third dimension, describe competencies such as persistence, dedication, and

discipline which facilitates task performance. Lastly, affective states, refers to quality of

emotional life and how emotional states like happiness, being optimistic and content,

facilitate one’s life course (Srivastava et al., 2008). In a follow-up study Sibia, Srivastava

and Misra (2005) elaborated this model further by integrating Mayer and Salovey’s (1997)

model (the four dimensions of identification, assimilation, understanding, and management

of emotions) and the above mentioned four dimensions of their indigenous EI model. The

test is available in both Hindi and English and retest reliability was reported as 0.75 for the

English, and 0.79 for the Hindi versions, respectively.

As argued previously, in our search for cultural universal and specific facets of the

construct, an cross-validation study of both a Western (e.g. Palmer et al, 2008 taxonomy)

and non-Western (Srivastava et al., 2008) taxonomy of EI on both Western and non-

Western cultural groups, should be conducted. Such a study may be the starting point in

providing answers to questions like whether a Western EI taxonomy is suitable for

identifying the structure of EI in non-Western groups, and if the same structure of EI is

found in non-Western and Western groups when indigenous and Western taxonomies are

cross-validated?

Future cross-cultural EI research should also be aimed at the quantification of bias and

equivalence. Suspected biasing factors should be measured (Van de Vijver & Leung, 2000).

By including a measure of social desirability or language proficiency (i.e. mastery of the

test language) together with the EI measure in the design of a study, the presence or

absence of a particular type of bias may be confirmed / rejected. The measurement of

contextual factors (i.e. including explanatory variables) may also assist in verifying (or

rejecting) particular interpretations of cross-cultural differences. Such better designed

210

cross-cultural studies, would assist in limiting post-hoc speculative unvalidated

interpretations, often found in exploratory cross-cultural studies. A monotrait-multimethod

research design could also be used to empirically examine bias (Van de Vijver & Leung,

2000).

Another distinction that should be accounted for in future research is the differentiation

between collectivist cultures and collectivist individual orientations. It is entirely possible

for individuals, or groups of individuals in a culture with traditionally collectivist norms, to

score low on collectivism (i.e. high on individualism). For example, McAuliffe, Jetten,

Hornsy and Hogg (2003) report evidence across two experimental studies which support

the notion of attenuation of the preference for collectivist over individualist group member

behaviour, when group norms prescribe individualism. That is, individualist group member

behaviour is promoted and collectivist group member behaviour reduced, when the group

norm represents individualism. In the current study, for example, the Sri-Lanka data was

collected in a single multinational company in Sri-Lanka (all the other datasets were

collected in multiple organisations). It may therefore be that the company culture (e.g.

individualistic norms) could be attenuating the influence of the national Sri-Lanka

collectivistic cultural value dimensions on individual behaviour, in this company. Future

studies should aim to incorporate multiple samples from different organisations to control

for this possible influence. In addition, future cross-cultural EI studies that are conducted in

multinational companies should measure company culture norms. Ryan et al., (2000,

p.557), for example, note that, “...within a global company there will be variability in the

extent to which local culture norms override or clash with company culture norms”. The

separation between national culture (e.g. Hofstede dimensions) and company culture may

well be subject to acculatarisation forces as globalisation is on the increase. This should be

an important consideration when cross-cultural EI research is conducted within the global

workplace.

211

5.4 Conclusion

This dissertation aimed to investigate the EI construct from a cross-cultural perspective.

The central research question was: to what extent do Hofstede (1980, 2001) cultural

dimensions systematically influence the cross-cultural transportability of a self report EI

measure? The results seem to suggest that cultural dimensions could be an important factor

that should be accounted for in the global assessment of EI. Cultural distance of the host

country (e.g. Sri Lanka) with the origin of the SUEIT (i.e. Australia) did influence the

validity transfer of the instrument. Evidence of method and item bias did emerge as

potential threats to the transportability of the instrument.

The current status of EI research on conceptualisation, assessment, and applications of the

construct, continues to be debated (e.g. Keele & Bell, 2008; Zeidner, Roberts, & Matthews,

2008). Chapter two of this dissertation provided a review of published cross-cultural

applications of prominent EI measures. Without a doubt, the factorial validity of these

measures remains a key issue to be addressed. In 2008, Zeidner et al., (2008, p.69) noted

that, “research is sorely needed to test for factorial invariance of current measures of EI

across sociocultural groups”. It is hoped that this dissertation has provided a starting point

of addressing this need. It may well be that cultural bias is a much more prevalent issue in

EI measurement, than generally thought. In addition, Zeidner et al., (2008) note that no

published DIF EI research study currently exist. This dissertation addressed this gap in EI

research. In conclusion, this dissertation illustrate that research which differentiates cultural

bias from true construct variance in self report EI measures, may help address the

assessment issues that currently surround the construct and hinder its practical utility.

212

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261

APPENDIX 1

Cover letters to request participation

Beste Departementshoof: Prof….

OPROEP OM DEELNAME IN NAVORSING

Geliewe my die geleentheid te gun om met hierdie skrywe ‘n versoek aan u te rig om die

meegaande kennisgewing (‘n oproep om deelname aan my PhD studie) aan al die personeel in u

departement uit te stuur. Die internasionale studie in organisasiegedrag word in samewerking met

navorsers aan die Swinburne Universiteit in Melbourne, Australie, onderneem.

Die etiese komittee van die Universiteit Stellenbosch het die studie en meegaande vraelys op 5

April 2005 goedgekeur. Vir u doeleindes heg ek die brief van goedkeuring van die etiese komittee

aan.

Die prosedure vir deelname word in die meegaande kennisgewing verduidelik. Neem asb kennis

dat deelname ten volle vrywillig is. Terugvoering sal op versoek aan respondente beskikbaar wees

(voorsiening word daarvoor aan die einde van die vraelys gemaak).

U samewerking met die uitstuur van die meegaande kennisgewing (wat die skakel na die webwerf

met die vraelys, wat op die US se intranet le) word hoog op prys gestel.

Baie dankie vir u tyd en belangrike bydrae tot hierdie internasionale studie!

Beste wense,

Gina Ekermans (PhD student: Universiteit van Stellenbosch

Prof C Stough (Instituut vir Neurologiese navorsing, Swinburne Universiteit van Tegnologie)

Dear Head of Department: Prof…

CALL TO PARTICIPATE IN RESEARCH With this letter, please afford me the opportunity to request your assistance in distributing the

attached notice (a call to participate in my PhD study) to all the personnel in your department. This

international study in organizational behaviour is being conducted in conjunction with researchers at

Swinburne University in Melbourne, Australia.

The ethical committee of Stellenbosch University approved the study and attached questionnaire on

5 April 2005. I attach the letter of approval of the ethical committee for your perusal. The procedure

to participate in the study is outlined in the attached notice. Please note that participation is

completely voluntary. Respondents will have the opportunity to request feedback on their results at

the end of the questionnaire, should they wish to do so.

Your assistance in forwarding the notice (that contains the cover letter and link providing access to

the webpage with the questionnaire, located on the US intranet) will be greatly appreciated.

Thank you for your time and important contribution to this international study!

Best wishes,

Gina Ekermans (PhD student: Stellenbosch University)

Prof C Stough (Brain Sciences Institute, Swinburne University of Technology)

262

NOTICE CALL TO PARTICIPATE IN RESEARCH: INTERNATIONAL STUDY ON ORGANISATIONAL BEHAVIOUR Dear staff member, In order to conduct joint international research between Stellenbosch University and researchers

at the Brain Sciences Institute at the Swinburne University of Technology in Melbourne,

Australia, we need information regarding your behaviour in the organisation where you work. We

therefore request your help with the completion of a questionnaire. In responding to the

questionnaire you will provide us with information about the way you see yourself typically dealing

with emotions in the workplace, as well as other information regarding your preferences and/or

current situation. Please note that your participation in the study is completely voluntary. You can decide for

yourself whether you will participate by choosing to complete the questionnaire. All responses will

be treated with anonymity and confidentiality and will only be used for the research purposes of this

project.

To access the questionnaire please click on the following link (Internet access will be needed): http://academic.sun.ac.za/bedryfsnav/index.asp

At the end of the questionnaire you can request feedback by simply filling in your name and e-mail

address when prompted to do so.

Thank you for your time and important contribution to this international study!

Best wishes, Gina Ekermans (PhD student: Swinburne University of Technology) Prof C Stough (Brain Sciences Institute, Swinburne University of Technology)

Please note: for technical support phone (021) 808 3596 or e-mail: [email protected]

263

APPENDIX 2

Descriptive statistics for SUEIT data utilised in chapter 4

This appendix includes the descriptive statistics for the raw SUEIT data utilised to fit the

modified measurement model with 50 items (M2a). It also contains the descriptive statistics

for the matched Australian samples randomly drawn from the n=3224 Australian sample

for the purposes of the measurement invariance analyses.

AUSTRALIAN SAMPLE A

Total Sample Size = 1604

Univariate Summary Statistics for Continuous Variables

Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq.

-------- ---- -------- ------- -------- -------- ------- ----- ------- -----

i1 3.976 0.504 316.010 -0.337 2.277 1.000 1 5.000 176

i2 3.774 0.760 198.762 -0.380 0.228 1.000 6 5.000 237

i4 3.748 0.755 198.771 -0.617 0.654 1.000 8 5.000 191

i5 4.015 0.604 266.033 -0.380 1.241 1.000 2 5.000 285

i6 3.282 0.764 172.132 -0.272 0.371 1.000 27 5.000 57

i7 3.401 0.919 148.242 -0.262 -0.237 1.000 35 5.000 162

i8 3.581 0.747 191.998 -0.377 0.064 1.000 5 5.000 120

i9 3.774 0.762 198.424 -0.333 0.108 1.000 5 5.000 243

i10 3.307 0.861 153.767 -0.347 -0.523 1.000 21 5.000 70

i11 3.448 0.799 172.707 -0.540 0.269 1.000 24 5.000 84

i13 3.881 0.627 247.882 -0.317 0.686 1.000 2 5.000 206

i14 3.416 0.842 162.409 -0.225 -0.348 1.000 12 5.000 121

i15 3.525 0.760 185.743 -0.370 -0.083 1.000 5 5.000 103

i16 3.858 0.731 211.240 -0.788 1.249 1.000 7 5.000 232

i17 3.554 0.706 201.665 -0.537 0.398 1.000 8 5.000 76

i19 4.223 0.680 248.588 -0.668 0.737 1.000 1 5.000 561

i20 4.028 0.735 219.518 -0.573 0.429 1.000 1 5.000 401

i22 3.901 0.668 233.886 -0.601 1.332 1.000 5 5.000 234

i23 3.818 0.599 255.344 -0.344 0.645 1.000 1 5.000 144

i25 3.430 0.833 164.835 -0.190 -0.022 1.000 20 5.000 136

i26 3.482 0.799 174.606 -0.371 -0.052 1.000 12 5.000 109

i27 3.966 0.680 233.454 -0.636 1.235 1.000 3 5.000 291

i28 3.245 0.807 161.139 -0.294 -0.017 1.000 30 5.000 55

i29 4.360 0.717 243.619 -1.022 1.056 1.000 2 5.000 770

i31 3.373 0.760 177.818 -0.245 0.202 1.000 17 5.000 75

i32 3.823 0.783 195.447 -0.388 0.107 1.000 6 5.000 290

i33 3.638 0.694 209.941 -0.273 0.342 1.000 6 5.000 127

i34 4.181 0.610 274.702 -0.581 1.938 1.000 3 5.000 444

i36 3.971 0.757 210.187 -0.696 0.972 1.000 8 5.000 358

i37 3.165 0.833 152.118 -0.175 -0.325 1.000 30 5.000 52

i38 3.883 0.655 237.543 -0.730 1.559 1.000 3 5.000 200

i39 3.564 0.641 222.677 -0.310 0.235 1.000 4 5.000 65

i41 3.779 0.740 204.434 -0.823 1.310 1.000 12 5.000 183

i42 3.430 0.812 169.145 -0.276 -0.059 1.000 16 5.000 110

i43 4.193 0.589 285.134 -0.330 0.973 1.000 1 5.000 450

i44 3.918 0.809 193.872 -0.698 0.579 1.000 8 5.000 353

i45 3.993 0.685 233.470 -0.329 0.110 2.000 29 5.000 341

i46 4.022 0.702 229.409 -0.528 0.762 1.000 4 5.000 371

i48 4.039 0.666 242.987 -0.654 1.466 1.000 3 5.000 343

i49 3.645 0.785 186.039 -0.425 0.303 1.000 12 5.000 175

i52 3.708 0.659 225.371 -0.429 0.365 1.000 1 5.000 120

i54 3.784 0.763 198.528 -0.523 0.383 1.000 5 5.000 228

i56 3.531 0.736 192.208 -0.557 0.357 1.000 11 5.000 80

i57 3.579 0.751 190.735 -0.229 0.122 1.000 8 5.000 141

i58 3.027 0.802 151.093 0.060 0.155 1.000 37 5.000 52

i59 3.741 0.603 248.326 -0.581 1.237 1.000 5 5.000 98

264

i60 3.817 0.681 224.358 -0.357 0.474 1.000 3 5.000 204

i61 3.434 0.792 173.604 -0.461 -0.190 1.000 11 5.000 74

i63 3.853 0.632 244.348 -0.736 1.654 1.000 3 5.000 164

i64 3.878 0.691 224.659 -0.458 0.654 1.000 3 5.000 245

AUSTRALIAN SAMPLE B



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 3.993 0.510 313.778 -0.379 2.724 1.000 2 5.000 192 i2 3.793 0.747 203.527 -0.400 0.339 1.000 6 5.000 237 i4 3.750 0.727 206.677 -0.543 0.573 1.000 5 5.000 179 i5 4.060 0.601 270.720 -0.300 0.899 1.000 1 5.000 326 i6 3.281 0.756 173.913 -0.178 0.318 1.000 22 5.000 61 i7 3.411 0.903 151.418 -0.287 -0.262 1.000 29 5.000 150 i8 3.611 0.739 195.833 -0.465 0.297 1.000 7 5.000 122 i9 3.748 0.784 191.419 -0.409 0.212 1.000 8 5.000 236 i10 3.321 0.842 157.968 -0.317 -0.468 1.000 17 5.000 72 i11 3.447 0.795 173.612 -0.491 0.196 1.000 21 5.000 86 i13 3.886 0.626 248.779 -0.432 1.182 1.000 4 5.000 203 i14 3.460 0.848 163.509 -0.394 -0.048 1.000 22 5.000 126 i15 3.561 0.769 185.623 -0.600 0.492 1.000 16 5.000 106 i16 3.865 0.743 208.488 -0.921 1.780 1.000 14 5.000 238 i17 3.603 0.677 213.359 -0.638 0.587 1.000 6 5.000 72 i19 4.190 0.707 237.535 -0.903 1.851 1.000 7 5.000 531 i20 4.023 0.770 209.191 -0.727 0.857 1.000 7 5.000 418 i22 3.927 0.658 239.103 -0.698 1.801 1.000 6 5.000 240 i23 3.811 0.618 247.061 -0.252 0.274 2.000 25 5.000 158 i25 3.426 0.841 163.191 -0.265 -0.070 1.000 21 5.000 127 i26 3.559 0.773 184.328 -0.391 0.139 1.000 10 5.000 127 i27 3.969 0.689 230.800 -0.716 1.506 1.000 5 5.000 296 i28 3.307 0.817 162.252 -0.315 0.073 1.000 30 5.000 74 i29 4.404 0.731 241.450 -1.339 2.354 1.000 7 5.000 833 i31 3.390 0.774 175.568 -0.281 0.168 1.000 18 5.000 82 i32 3.844 0.762 202.143 -0.416 0.318 1.000 7 5.000 286 i33 3.669 0.717 205.096 -0.320 0.316 1.000 6 5.000 151 i34 4.204 0.632 266.538 -0.683 1.948 1.000 4 5.000 488 i36 3.958 0.772 205.384 -0.636 0.662 1.000 7 5.000 366 i37 3.185 0.831 153.524 -0.168 -0.420 1.000 24 5.000 53 i38 3.924 0.623 252.300 -0.752 2.250 1.000 5 5.000 208 i39 3.544 0.659 215.512 -0.182 0.064 1.000 3 5.000 76 i41 3.784 0.728 208.346 -0.821 1.160 1.000 7 5.000 175 i42 3.457 0.796 174.005 -0.272 -0.031 1.000 13 5.000 112 i43 4.191 0.594 282.712 -0.318 0.818 1.000 1 5.000 453 i44 3.948 0.833 189.914 -0.797 0.815 1.000 14 5.000 393 i45 4.044 0.694 233.565 -0.441 0.344 1.000 1 5.000 390 i46 4.007 0.722 222.228 -0.567 0.815 1.000 6 5.000 374 i48 4.090 0.681 240.720 -0.826 2.049 1.000 6 5.000 396 i49 3.651 0.776 188.424 -0.459 0.468 1.000 14 5.000 171 i52 3.744 0.634 236.760 -0.524 0.748 1.000 2 5.000 117 i54 3.811 0.747 204.437 -0.622 0.712 1.000 6 5.000 225 i56 3.583 0.699 205.254 -0.696 0.960 1.000 14 5.000 76 i57 3.594 0.772 186.577 -0.324 0.212 1.000 11 5.000 152 i58 3.070 0.811 151.671 0.032 0.089 1.000 35 5.000 58 i59 3.779 0.577 262.258 -0.432 0.706 1.000 1 5.000 105 i60 3.800 0.708 214.921 -0.304 0.173 1.000 2 5.000 218 i61 3.450 0.773 178.911 -0.368 -0.153 1.000 8 5.000 82 i63 3.895 0.627 248.925 -0.847 2.335 1.000 5 5.000 184 i64 3.869 0.733 211.505 -0.514 0.652 1.000 6 5.000 269

265

NEW ZEALAND




-------- ---- -------- ------- -------- -------- ------- ----- ------- -----

i1 4.004 0.495 123.848 0.010 1.155 3.000 28 5.000 29

i2 3.808 0.663 87.884 -0.123 -0.081 2.000 4 5.000 29

i4 3.825 0.699 83.713 -0.963 2.251 1.000 2 5.000 26

i5 4.077 0.610 102.287 -0.155 0.129 2.000 1 5.000 52

i6 3.239 0.749 66.187 -0.361 -0.350 1.000 2 5.000 3

i7 3.462 0.889 59.561 -0.271 -0.098 1.000 4 5.000 25

i8 3.628 0.683 81.275 -0.269 -0.022 2.000 11 5.000 16

i9 3.816 0.703 83.059 -0.026 -0.406 2.000 4 5.000 36

i10 3.282 0.790 63.558 -0.178 -0.505 1.000 1 5.000 8

i11 3.440 0.769 68.463 -0.312 -0.468 2.000 29 5.000 11

i13 3.897 0.546 109.132 -0.062 0.255 3.000 48 5.000 24

i14 3.513 0.804 66.870 -0.543 0.338 1.000 3 5.000 16

i15 3.509 0.701 76.567 -0.181 -0.210 2.000 16 5.000 12

i16 3.936 0.747 80.592 -0.768 1.214 1.000 1 5.000 45

i17 3.551 0.628 86.507 -0.562 -0.072 2.000 12 5.000 5

i19 4.308 0.593 111.181 -0.340 0.086 2.000 1 5.000 87

i20 4.094 0.655 95.628 -0.468 0.698 2.000 4 5.000 58

i22 3.991 0.579 105.540 -0.671 2.345 2.000 5 5.000 33

i23 3.748 0.594 96.568 -0.478 0.556 2.000 5 5.000 14

i25 3.363 0.819 62.851 -0.334 0.383 1.000 5 5.000 14

i26 3.654 0.783 71.355 -0.440 0.157 1.000 1 5.000 25

i27 4.026 0.641 96.014 -0.712 1.751 2.000 7 5.000 44

i28 3.338 0.730 69.893 -0.421 0.026 1.000 2 5.000 5

i29 4.432 0.734 92.404 -1.274 1.420 2.000 6 5.000 129

i31 3.359 0.769 66.803 -0.202 0.032 1.000 2 5.000 11

i32 3.902 0.726 82.234 -0.256 0.214 1.000 1 5.000 46

i33 3.684 0.695 81.061 -0.255 0.451 1.000 1 5.000 22

i34 4.235 0.579 111.917 -0.203 0.293 2.000 1 5.000 72

i36 4.009 0.741 82.736 -0.715 1.139 1.000 1 5.000 55

i37 3.154 0.754 63.986 -0.141 -0.645 1.000 1 5.000 3

i38 3.983 0.571 106.727 -0.980 4.466 1.000 1 5.000 30

i39 3.538 0.636 85.149 -0.246 0.468 1.000 1 5.000 9

i41 3.803 0.671 86.687 -0.434 0.465 2.000 8 5.000 26

i42 3.551 0.764 71.135 -0.495 0.404 1.000 2 5.000 16

i43 4.188 0.627 102.105 -0.794 2.937 1.000 1 5.000 67

i44 3.923 0.777 77.250 -0.586 0.268 2.000 13 5.000 49

i45 4.090 0.659 94.967 -0.460 0.632 2.000 4 5.000 58

i46 4.034 0.680 90.757 -0.373 0.235 2.000 4 5.000 54

i48 4.137 0.613 103.189 -0.536 2.245 1.000 1 5.000 59

i49 3.620 0.739 74.954 -0.226 -0.174 2.000 15 5.000 21

i52 3.718 0.673 84.567 -1.132 2.783 1.000 3 5.000 14

i54 3.829 0.727 80.564 -0.605 0.547 2.000 13 5.000 32

i56 3.607 0.687 80.363 -0.672 0.678 1.000 1 5.000 11

i57 3.615 0.722 76.623 -0.237 -0.128 2.000 14 5.000 19

i58 2.996 0.826 55.475 -0.176 0.050 1.000 9 5.000 5

i59 3.765 0.556 103.553 -0.633 0.862 2.000 4 5.000 11

i60 3.850 0.680 86.608 -0.714 2.054 1.000 2 5.000 30

i61 3.449 0.780 67.605 -0.429 0.057 1.000 2 5.000 12

i63 3.940 0.626 96.359 -0.488 1.087 2.000 5 5.000 34

i64 3.889 0.750 79.303 -0.492 0.228 2.000 11 5.000 43

266

UNITED STATES OF AMERICA



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.084 0.658 105.068 -0.460 0.639 2.000 5 5.000 70 i2 3.815 0.800 80.757 -0.396 0.245 1.000 2 5.000 54 i4 3.648 0.872 70.877 -0.462 0.159 1.000 4 5.000 42 i5 4.084 0.753 91.922 -0.882 1.920 1.000 3 5.000 82 i6 3.192 0.781 69.229 -0.039 0.137 1.000 4 5.000 11 i7 3.429 1.011 57.456 -0.407 -0.400 1.000 10 5.000 36 i8 3.669 0.868 71.597 -0.466 0.207 1.000 4 5.000 44 i9 3.666 0.908 68.376 -0.332 -0.262 1.000 3 5.000 52 i10 3.366 0.886 64.347 -0.575 0.116 1.000 9 5.000 16 i11 3.251 0.931 59.150 -0.336 -0.083 1.000 12 5.000 19 i13 3.937 0.726 91.837 -0.786 1.985 1.000 3 5.000 54 i14 3.460 0.915 64.081 -0.322 -0.377 1.000 4 5.000 30 i15 3.645 0.848 72.795 -0.599 0.239 1.000 3 5.000 34 i16 3.944 0.859 77.774 -0.959 1.225 1.000 4 5.000 70 i17 3.624 0.770 79.776 -0.259 0.002 1.000 1 5.000 30 i19 4.247 0.747 96.373 -0.945 1.241 1.000 1 5.000 115 i20 4.073 0.805 85.689 -0.862 1.065 1.000 2 5.000 88 i22 4.042 0.737 92.862 -0.698 1.023 1.000 1 5.000 73 i23 3.767 0.737 86.576 -0.392 0.668 1.000 2 5.000 39 i25 3.463 0.941 62.338 -0.210 -0.267 1.000 6 5.000 39 i26 3.547 0.907 66.275 -0.651 0.491 1.000 9 5.000 32 i27 4.091 0.766 90.514 -0.767 0.888 1.000 1 5.000 86 i28 3.261 0.868 63.664 -0.339 0.111 1.000 9 5.000 15 i29 4.449 0.666 113.110 -1.170 1.605 2.000 5 5.000 152 i31 3.380 0.888 64.468 -0.190 -0.053 1.000 6 5.000 27 i32 3.871 0.928 70.678 -0.613 0.083 1.000 4 5.000 78 i33 3.700 0.885 70.798 -0.622 0.545 1.000 6 5.000 47 i34 4.261 0.698 103.404 -0.780 0.746 2.000 6 5.000 111 i36 3.864 0.908 72.110 -0.857 0.857 1.000 6 5.000 67 i37 3.063 0.959 54.120 -0.342 -0.381 1.000 19 5.000 10 i38 3.787 0.789 81.324 -0.722 0.806 1.000 2 5.000 41 i39 3.638 0.767 80.331 -0.635 1.035 1.000 4 5.000 26 i41 3.791 0.876 73.316 -0.554 0.034 1.000 2 5.000 57 i42 3.418 0.904 64.042 -0.239 -0.243 1.000 5 5.000 29 i43 4.314 0.636 114.855 -0.790 1.443 2.000 5 5.000 112 i44 4.129 0.870 80.446 -1.185 1.850 1.000 5 5.000 105 i45 4.136 0.728 96.205 -0.707 0.957 1.000 1 5.000 90 i46 4.150 0.808 87.046 -0.721 0.227 1.000 1 5.000 108 i48 4.136 0.775 90.429 -0.967 1.598 1.000 2 5.000 94 i49 3.707 0.903 69.522 -0.506 0.185 1.000 5 5.000 53 i52 3.878 0.721 91.104 -0.545 0.815 1.000 1 5.000 47 i54 3.767 0.884 72.205 -0.566 0.139 1.000 3 5.000 55 i56 3.575 0.841 72.038 -0.629 0.663 1.000 6 5.000 28 i57 3.505 0.892 66.546 -0.313 -0.052 1.000 5 5.000 34 i58 2.902 0.922 53.314 0.034 -0.332 1.000 16 5.000 10 i59 3.791 0.688 93.319 -0.415 0.722 1.000 1 5.000 34 i60 3.847 0.800 81.411 -0.334 -0.300 2.000 15 5.000 58 i61 3.474 0.864 68.106 -0.590 0.355 1.000 7 5.000 22 i63 3.850 0.759 85.990 -0.759 1.200 1.000 2 5.000 45 i64 3.857 0.830 78.704 -0.466 -0.210 2.000 20 5.000 61

267

ITALIAN



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 3.462 0.848 73.016 -0.487 0.790 1.000 10 5.000 28 i2 3.138 1.125 49.876 -0.287 -0.464 1.000 36 5.000 35 i4 2.841 1.072 47.383 -0.031 -0.799 1.000 37 5.000 14 i5 3.700 1.067 62.030 -0.749 0.143 1.000 16 5.000 76 i6 3.244 1.202 48.270 -0.163 -0.853 1.000 28 5.000 57 i7 3.034 1.217 44.595 -0.108 -0.831 1.000 45 5.000 41 i8 3.253 1.106 52.597 -0.179 -0.570 1.000 22 5.000 46 i9 3.591 1.124 57.120 -0.421 -0.639 1.000 13 5.000 80 i10 3.038 1.211 44.882 -0.072 -0.973 1.000 38 5.000 38 i11 3.359 1.139 52.772 -0.175 -0.782 1.000 17 5.000 61 i13 3.578 0.947 67.594 -0.561 0.311 1.000 11 5.000 48 i14 3.266 1.164 50.174 -0.279 -0.736 1.000 27 5.000 48 i15 2.947 1.120 47.071 -0.124 -0.570 1.000 43 5.000 26 i16 3.878 0.989 70.120 -0.750 0.162 1.000 7 5.000 95 i17 3.434 0.951 64.588 -0.283 -0.167 1.000 9 5.000 40 i19 3.612 1.139 56.729 -0.696 -0.213 1.000 21 5.000 73 i20 3.447 1.010 61.079 -0.544 -0.101 1.000 15 5.000 39 i22 3.825 1.123 60.937 -0.867 0.069 1.000 16 5.000 103 i23 3.597 1.016 63.358 -0.464 -0.088 1.000 12 5.000 64 i25 3.188 1.058 53.919 -0.093 -0.499 1.000 19 5.000 37 i26 2.694 1.134 42.505 0.169 -0.658 1.000 56 5.000 21 i27 3.744 1.178 56.831 -0.763 -0.282 1.000 19 5.000 100 i28 2.700 1.102 43.840 0.064 -0.720 1.000 54 5.000 15 i29 3.638 1.059 61.427 -0.843 0.259 1.000 18 5.000 59 i31 3.406 1.096 55.596 -0.169 -0.650 1.000 14 5.000 62 i32 3.725 1.082 61.557 -0.644 -0.177 1.000 13 5.000 87 i33 3.441 0.948 64.903 -0.228 -0.124 1.000 9 5.000 43 i34 3.716 0.929 71.571 -0.917 1.182 1.000 13 5.000 54 i36 3.119 1.096 50.883 -0.309 -0.648 1.000 30 5.000 25 i37 3.069 1.104 49.744 -0.094 -0.560 1.000 30 5.000 33 i38 3.619 0.953 67.954 -0.727 0.606 1.000 13 5.000 49 i39 3.231 0.932 62.043 0.039 -0.166 1.000 9 5.000 31 i41 3.712 0.963 68.996 -0.840 0.619 1.000 11 5.000 58 i42 3.281 1.021 57.477 -0.088 -0.515 1.000 12 5.000 40 i43 3.766 0.870 77.432 -0.676 0.575 1.000 5 5.000 57 i44 3.559 1.061 60.031 -0.696 0.139 1.000 20 5.000 56 i45 3.525 0.956 65.931 -0.569 0.318 1.000 13 5.000 43 i46 3.928 1.019 68.947 -0.839 0.281 1.000 9 5.000 109 i48 3.612 0.896 72.144 -0.552 0.560 1.000 9 5.000 46 i49 3.638 1.074 60.587 -0.552 -0.223 1.000 14 5.000 75 i52 3.534 0.859 73.566 -0.167 -0.049 1.000 4 5.000 41 i54 3.237 1.062 54.520 -0.328 -0.465 1.000 22 5.000 32 i56 3.850 0.918 75.027 -0.750 0.522 1.000 6 5.000 77 i57 3.191 1.007 56.688 -0.186 -0.237 1.000 19 5.000 30 i58 3.047 0.977 55.805 0.007 -0.162 1.000 19 5.000 24 i59 3.631 0.857 75.765 -0.593 0.672 1.000 7 5.000 41 i60 3.281 0.968 60.660 0.017 -0.249 1.000 10 5.000 39 i61 3.062 0.996 54.978 -0.145 -0.236 1.000 23 5.000 22 i63 3.806 0.903 75.421 -0.868 1.107 1.000 9 5.000 65 i64 3.194 0.976 58.525 -0.254 -0.140 1.000 18 5.000 25

268

SOUTH AFRICA (WHITE)




-------- ---- -------- ------- -------- -------- ------- ----- ------- -----

i1 3.817 0.648 100.333 -0.419 1.027 1.000 1 5.000 32

i2 3.645 0.807 76.871 -0.379 0.131 1.000 2 5.000 35

i4 3.797 0.882 73.274 -0.808 0.763 1.000 5 5.000 54

i5 3.921 0.704 94.858 -0.368 0.537 1.000 1 5.000 54

i6 3.248 0.963 57.429 -0.164 -0.176 1.000 12 5.000 27

i7 3.079 1.021 51.368 -0.238 -0.446 1.000 22 5.000 18

i8 3.431 0.932 62.680 -0.365 -0.423 1.000 5 5.000 28

i9 3.300 0.961 58.477 -0.397 -0.232 1.000 12 5.000 22

i10 3.069 0.964 54.209 -0.092 -0.656 1.000 12 5.000 14

i11 3.217 0.990 55.335 -0.167 -0.632 1.000 10 5.000 23

i13 3.786 0.667 96.602 -0.851 2.024 1.000 2 5.000 26

i14 3.255 0.947 58.543 -0.382 -0.365 1.000 11 5.000 17

i15 3.479 0.857 69.118 -0.632 0.410 1.000 7 5.000 21

i16 3.455 0.970 60.660 -0.331 -0.560 1.000 5 5.000 35

i17 3.510 0.816 73.252 -0.380 -0.097 1.000 2 5.000 23

i19 4.055 0.787 87.718 -0.526 -0.153 2.000 10 5.000 88

i20 3.762 0.799 80.164 -0.528 0.223 1.000 1 5.000 43

i22 3.524 0.881 68.117 -0.380 -0.378 1.000 2 5.000 30

i23 3.455 0.941 62.527 -0.522 -0.127 1.000 8 5.000 28

i25 3.272 0.969 57.497 -0.225 -0.603 1.000 8 5.000 23

i26 3.314 0.881 64.035 -0.139 -0.385 1.000 4 5.000 21

i27 3.638 0.983 63.054 -0.522 -0.413 1.000 4 5.000 51

i28 3.038 0.882 58.686 0.017 -0.255 1.000 9 5.000 12

i29 3.969 0.778 86.927 -0.613 0.291 2.000 15 5.000 68

i31 3.252 0.961 57.652 -0.216 -0.500 1.000 9 5.000 22

i32 3.441 1.061 55.221 -0.361 -0.510 1.000 12 5.000 46

i33 3.341 0.839 67.859 -0.429 0.043 1.000 6 5.000 14

i34 3.979 0.730 92.869 -0.506 0.320 2.000 10 5.000 64

i36 3.710 0.810 77.995 -0.604 0.395 1.000 2 5.000 37

i37 3.059 0.904 57.639 -0.144 -0.429 1.000 11 5.000 10

i38 3.666 0.777 80.319 -0.764 0.885 1.000 3 5.000 26

i39 3.252 0.764 72.494 -0.461 0.342 1.000 6 5.000 6

i41 3.834 0.869 75.175 -0.917 0.939 1.000 4 5.000 54

i42 3.152 0.801 67.024 -0.159 -0.347 1.000 4 5.000 7

i43 4.076 0.618 112.336 -0.314 0.620 2.000 3 5.000 64

i44 3.734 0.866 73.467 -0.521 -0.132 1.000 1 5.000 48

i45 4.017 0.718 95.303 -0.534 0.437 2.000 9 5.000 68

i46 3.683 1.067 58.796 -0.562 -0.368 1.000 9 5.000 71

i48 3.793 0.752 85.856 -0.572 0.845 1.000 2 5.000 41

i49 3.300 0.909 61.809 -0.463 0.016 1.000 11 5.000 17

i52 3.459 0.865 68.127 -0.324 -0.262 1.000 3 5.000 25

i54 3.510 0.873 68.442 -0.471 -0.071 1.000 4 5.000 27

i56 3.531 0.820 73.350 -0.575 0.514 1.000 5 5.000 23

i57 3.359 0.961 59.538 -0.371 -0.241 1.000 10 5.000 27

i58 2.869 0.886 55.124 -0.011 -0.264 1.000 16 5.000 7

i59 3.524 0.816 73.566 -0.348 -0.266 1.000 1 5.000 24

i60 3.483 0.908 65.299 -0.604 0.414 1.000 10 5.000 28

i61 3.155 0.904 59.456 -0.340 -0.481 1.000 10 5.000 9

i63 3.717 0.760 83.321 -0.572 0.688 1.000 2 5.000 33

i64 3.734 0.795 80.017 -0.612 0.534 1.000 2 5.000 38

269

SOUTH AFRICA (NON-WHITE)




-------- ---- -------- ------- -------- -------- ------- ----- ------- -----

i1 3.522 0.806 80.259 -0.159 -0.108 1.000 2 5.000 33

i2 3.777 0.997 69.520 -0.736 0.308 1.000 11 5.000 83

i4 3.510 1.086 59.344 -0.392 -0.535 1.000 14 5.000 66

i5 3.546 0.981 66.338 -0.548 0.112 1.000 13 5.000 51

i6 3.374 1.146 54.069 -0.267 -0.704 1.000 21 5.000 63

i7 2.893 1.152 46.091 -0.013 -0.641 1.000 50 5.000 31

i8 3.297 1.012 59.784 -0.362 -0.355 1.000 17 5.000 32

i9 3.481 1.066 59.917 -0.357 -0.473 1.000 14 5.000 61

i10 2.858 1.154 45.472 0.047 -0.842 1.000 46 5.000 26

i11 3.279 1.017 59.165 -0.189 -0.426 1.000 15 5.000 38

i13 3.774 0.901 76.890 -0.816 0.951 1.000 9 5.000 64

i14 3.151 1.060 54.594 -0.094 -0.557 1.000 21 5.000 35

i15 3.588 1.040 63.304 -0.578 -0.065 1.000 15 5.000 64

i16 3.335 0.996 61.493 -0.457 -0.190 1.000 17 5.000 31

i17 3.332 0.965 63.404 -0.467 0.039 1.000 17 5.000 29

i19 4.080 1.048 71.489 -1.270 1.250 1.000 14 5.000 142

i20 3.427 1.132 55.586 -0.364 -0.527 1.000 22 5.000 65

i22 3.362 1.083 57.007 -0.504 -0.381 1.000 23 5.000 41

i23 3.522 1.049 61.611 -0.502 -0.189 1.000 16 5.000 59

i25 3.288 1.204 50.130 -0.342 -0.748 1.000 34 5.000 56

i26 2.985 1.073 51.067 -0.159 -0.535 1.000 36 5.000 23

i27 3.383 1.099 56.520 -0.464 -0.411 1.000 23 5.000 48

i28 3.154 1.083 53.475 -0.296 -0.376 1.000 32 5.000 33

i29 3.878 1.018 69.957 -0.913 0.559 1.000 12 5.000 100

i31 3.223 1.108 53.404 -0.159 -0.545 1.000 25 5.000 47

i32 3.448 1.162 54.494 -0.406 -0.540 1.000 25 5.000 71

i33 3.386 1.060 58.611 -0.248 -0.393 1.000 17 5.000 55

i34 3.742 1.024 67.060 -0.819 0.377 1.000 14 5.000 77

i36 3.620 1.071 62.054 -0.718 0.082 1.000 19 5.000 68

i37 2.804 1.174 43.849 0.175 -0.693 1.000 52 5.000 33

i38 3.457 1.046 60.670 -0.513 -0.118 1.000 19 5.000 50

i39 3.211 0.970 60.773 -0.275 -0.134 1.000 18 5.000 26

i41 3.792 1.037 67.120 -0.831 0.367 1.000 14 5.000 89

i42 3.098 1.023 55.581 -0.114 -0.366 1.000 23 5.000 28

i43 3.739 0.915 75.051 -0.939 1.118 1.000 11 5.000 56

i44 3.801 1.052 66.347 -0.753 0.013 1.000 11 5.000 95

i45 3.911 0.918 78.185 -0.866 0.913 1.000 8 5.000 91

i46 3.516 1.134 56.918 -0.582 -0.307 1.000 24 5.000 67

i48 3.641 0.922 72.503 -0.672 0.522 1.000 10 5.000 51

i49 3.368 1.145 54.006 -0.421 -0.529 1.000 27 5.000 55

i52 3.377 0.972 63.808 -0.306 -0.126 1.000 13 5.000 39

i54 3.457 1.128 56.253 -0.480 -0.474 1.000 22 5.000 61

i56 3.377 1.016 60.988 -0.381 -0.184 1.000 17 5.000 42

i57 3.365 1.123 54.983 -0.325 -0.584 1.000 22 5.000 56

i58 2.691 1.032 47.872 0.336 -0.307 1.000 38 5.000 19

i59 3.454 0.909 69.767 -0.377 0.193 1.000 10 5.000 37

i60 3.433 1.116 56.456 -0.490 -0.364 1.000 24 5.000 57

i61 2.887 1.123 47.199 0.122 -0.669 1.000 38 5.000 30

i63 3.460 1.120 56.695 -0.557 -0.388 1.000 23 5.000 56

i64 3.706 1.023 66.481 -0.710 0.170 1.000 13 5.000 75

270

SRI LANKA



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 3.927 0.741 128.373 -0.539 0.900 1.000 4 5.000 119 i2 3.804 0.851 108.292 -0.397 -0.071 1.000 4 5.000 123 i4 3.683 0.941 94.815 -0.513 -0.083 1.000 10 5.000 110 i5 3.922 0.833 114.093 -0.368 -0.319 1.000 2 5.000 155 i6 3.499 0.948 89.419 -0.022 -0.398 1.000 10 5.000 103 i7 3.462 0.995 84.311 -0.170 -0.442 1.000 15 5.000 97 i8 3.470 0.901 93.264 -0.282 -0.058 1.000 12 5.000 68 i9 3.482 0.867 97.299 -0.047 -0.229 1.000 6 5.000 73 i10 3.232 0.937 83.562 -0.314 -0.272 1.000 23 5.000 36 i11 3.307 0.898 89.190 -0.089 -0.403 1.000 9 5.000 48 i13 4.029 0.704 138.638 -0.541 0.873 1.000 2 5.000 139 i14 3.402 0.893 92.320 -0.013 -0.263 1.000 8 5.000 68 i15 3.516 0.812 104.884 -0.628 0.695 1.000 12 5.000 42 i16 3.230 0.949 82.446 -0.113 -0.395 1.000 18 5.000 48 i17 3.733 0.789 114.583 -0.637 0.680 1.000 5 5.000 75 i19 3.942 0.867 110.218 -0.662 0.369 1.000 6 5.000 161 i20 3.811 0.814 113.468 -0.613 0.662 1.000 6 5.000 104 i22 3.622 0.890 98.609 -0.466 0.129 1.000 10 5.000 85 i23 3.777 0.838 109.162 -0.572 0.493 1.000 7 5.000 104 i25 3.293 1.022 78.096 -0.312 -0.270 1.000 33 5.000 64 i26 3.336 0.837 96.512 -0.294 -0.058 1.000 10 5.000 33 i27 3.610 0.974 89.816 -0.667 0.103 1.000 18 5.000 90 i28 3.440 0.881 94.613 -0.529 0.304 1.000 17 5.000 47 i29 3.898 0.773 122.218 -0.535 0.404 1.000 2 5.000 118 i31 3.366 0.859 94.962 -0.051 -0.278 1.000 6 5.000 51 i32 3.535 0.974 87.922 -0.315 -0.255 1.000 15 5.000 98 i33 3.555 0.776 110.993 -0.405 0.400 1.000 6 5.000 48 i34 3.591 0.880 98.863 -0.852 0.932 1.000 19 5.000 59 i36 3.542 0.974 88.116 -0.680 0.092 1.000 21 5.000 73 i37 3.281 0.943 84.338 -0.329 -0.172 1.000 23 5.000 44 i38 3.801 0.739 124.673 -0.558 0.630 1.000 2 5.000 80 i39 3.421 0.731 113.390 -0.184 0.492 1.000 6 5.000 31 i41 3.634 0.849 103.713 -0.448 0.012 1.000 5 5.000 75 i42 3.361 0.803 101.470 -0.168 0.150 1.000 8 5.000 37 i43 3.714 0.730 123.329 -0.452 0.426 1.000 2 5.000 62 i44 3.814 0.843 109.583 -0.734 0.799 1.000 8 5.000 108 i45 3.913 0.730 129.813 -0.656 1.123 1.000 3 5.000 106 i46 3.460 0.931 90.051 -0.182 -0.290 1.000 11 5.000 78 i48 3.894 0.671 140.720 -0.556 1.241 1.000 2 5.000 86 i49 3.445 0.867 96.209 -0.207 0.002 1.000 10 5.000 60 i52 3.509 0.806 105.489 -0.237 0.123 1.000 6 5.000 54 i54 3.489 0.863 97.922 -0.517 0.216 1.000 12 5.000 50 i56 3.358 0.819 99.355 -0.332 0.380 1.000 13 5.000 35 i57 3.496 0.930 91.078 -0.192 -0.417 1.000 8 5.000 82 i58 3.172 1.010 76.102 0.129 -0.551 1.000 19 5.000 67 i59 3.593 0.772 112.685 -0.447 0.273 1.000 4 5.000 50 i60 3.664 0.908 97.802 -0.400 -0.048 1.000 9 5.000 104 i61 3.138 0.890 85.456 -0.230 -0.159 1.000 22 5.000 25 i63 3.802 0.791 116.398 -0.670 1.017 1.000 7 5.000 94 i64 3.831 0.822 112.877 -0.492 0.231 1.000 4 5.000 117

271

AUSTRALIAN (N=234) MATCHED TO NEW ZEALAND (N=234)




-------- ---- -------- ------- -------- -------- ------- ----- ------- -----

i1 3.979 0.511 119.052 -1.007 6.128 1.000 1 5.000 24

i2 3.799 0.692 83.988 -0.338 0.204 2.000 8 5.000 29

i4 3.692 0.735 76.854 -0.426 0.439 1.000 1 5.000 24

i5 4.098 0.551 113.778 -0.261 1.588 2.000 2 5.000 46

i6 3.231 0.734 67.329 0.002 0.253 1.000 2 5.000 8

i7 3.355 0.902 56.894 -0.231 -0.193 1.000 5 5.000 20

i8 3.598 0.748 73.623 -0.434 0.238 1.000 1 5.000 18

i9 3.671 0.838 67.002 -0.243 -0.037 1.000 2 5.000 37

i10 3.368 0.782 65.895 -0.368 -0.138 1.000 2 5.000 9

i11 3.376 0.846 61.018 -0.462 -0.095 1.000 4 5.000 12

i13 3.880 0.651 91.224 -0.254 0.246 2.000 4 5.000 33

i14 3.355 0.807 63.628 -0.138 -0.139 1.000 2 5.000 14

i15 3.496 0.719 74.360 -0.229 0.144 1.000 1 5.000 13

i16 3.885 0.741 80.209 -0.770 1.571 1.000 2 5.000 39

i17 3.590 0.637 86.172 -0.391 -0.010 2.000 10 5.000 9

i19 4.214 0.679 94.976 -0.790 1.297 2.000 6 5.000 78

i20 4.073 0.705 88.384 -0.622 1.170 1.000 1 5.000 61

i22 3.936 0.622 96.854 -0.498 1.140 2.000 5 5.000 33

i23 3.714 0.655 86.794 -0.367 0.251 2.000 8 5.000 18

i25 3.453 0.844 62.582 -0.197 -0.008 1.000 3 5.000 22

i26 3.560 0.734 74.150 -0.406 0.576 1.000 2 5.000 16

i27 4.026 0.635 97.032 -0.326 0.533 2.000 3 5.000 47

i28 3.265 0.751 66.463 -0.295 -0.524 1.000 1 5.000 4

i29 4.410 0.670 100.682 -0.790 -0.115 2.000 1 5.000 119

i31 3.312 0.798 63.520 -0.314 0.184 1.000 4 5.000 10

i32 3.833 0.804 72.962 -0.339 -0.035 1.000 1 5.000 47

i33 3.637 0.724 76.842 -0.761 1.439 1.000 3 5.000 16

i34 4.167 0.588 108.437 -0.306 0.907 2.000 2 5.000 61

i36 3.996 0.671 91.053 -0.510 0.786 2.000 6 5.000 46

i37 3.226 0.836 59.010 -0.225 -0.269 1.000 4 5.000 9

i38 3.850 0.641 91.874 -0.842 2.210 1.000 1 5.000 24

i39 3.556 0.628 86.661 -0.579 0.630 1.000 1 5.000 6

i41 3.791 0.696 83.360 -0.926 1.636 1.000 1 5.000 22

i42 3.487 0.809 65.945 -0.326 0.000 1.000 2 5.000 18

i43 4.248 0.577 112.579 -0.074 -0.430 3.000 17 5.000 75

i44 3.991 0.758 80.518 -0.581 0.316 2.000 10 5.000 56

i45 4.013 0.732 83.820 -0.284 -0.372 2.000 4 5.000 60

i46 3.957 0.763 79.359 -0.572 0.610 1.000 1 5.000 53

i48 4.085 0.730 85.568 -1.067 2.677 1.000 2 5.000 61

i49 3.585 0.799 68.635 -0.232 -0.108 1.000 1 5.000 25

i52 3.756 0.597 96.210 -0.343 0.357 2.000 4 5.000 16

i54 3.803 0.744 78.205 -0.485 0.231 2.000 13 5.000 33

i56 3.568 0.691 78.941 -0.601 0.487 1.000 1 5.000 10

i57 3.585 0.788 69.576 -0.073 -0.130 1.000 1 5.000 27

i58 3.004 0.816 56.346 -0.008 0.175 1.000 7 5.000 7

i59 3.744 0.603 95.028 -0.652 1.629 1.000 1 5.000 14

i60 3.812 0.717 81.347 -0.481 0.356 2.000 11 5.000 31

i61 3.521 0.771 69.894 -0.413 -0.019 1.000 1 5.000 15

i63 4.004 0.536 114.232 -0.333 1.819 2.000 2 5.000 32

i64 3.885 0.752 78.984 -0.112 -0.574 2.000 5 5.000 49

272

AUSTRALIAN (N=287) MATCHED TO USA (N=287)



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.017 0.498 136.681 -0.305 2.522 2.000 2 5.000 36 i2 3.843 0.709 91.774 -0.475 0.399 2.000 12 5.000 41 i4 3.704 0.738 85.007 -0.570 0.525 1.000 1 5.000 28 i5 4.052 0.574 119.608 -0.220 0.914 2.000 2 5.000 53 i6 3.247 0.732 75.109 -0.047 0.079 1.000 2 5.000 9 i7 3.470 0.900 65.347 -0.375 -0.157 1.000 5 5.000 29 i8 3.603 0.721 84.641 -0.364 0.249 1.000 1 5.000 21 i9 3.690 0.809 77.224 -0.372 0.150 1.000 2 5.000 40 i10 3.390 0.763 75.321 -0.316 -0.566 2.000 39 5.000 10 i11 3.460 0.769 76.186 -0.537 0.251 1.000 3 5.000 13 i13 3.857 0.656 99.583 -0.366 0.455 2.000 7 5.000 37 i14 3.401 0.859 67.086 -0.373 -0.052 1.000 5 5.000 20 i15 3.533 0.774 77.309 -0.566 0.433 1.000 3 5.000 18 i16 3.882 0.766 85.804 -0.922 1.750 1.000 3 5.000 48 i17 3.585 0.678 89.523 -0.888 1.047 1.000 2 5.000 9 i19 4.199 0.699 101.780 -0.851 1.325 2.000 9 5.000 95 i20 4.084 0.739 93.674 -0.711 0.985 1.000 1 5.000 81 i22 3.948 0.715 93.535 -0.906 2.118 1.000 2 5.000 51 i23 3.801 0.619 103.993 -0.110 -0.004 2.000 3 5.000 29 i25 3.425 0.865 67.058 -0.256 0.004 1.000 5 5.000 26 i26 3.557 0.777 77.534 -0.395 0.192 1.000 2 5.000 23 i27 3.937 0.666 100.147 -0.573 0.977 2.000 9 5.000 46 i28 3.289 0.773 72.064 -0.185 -0.242 1.000 2 5.000 10 i29 4.446 0.712 105.813 -1.536 3.622 1.000 2 5.000 156 i31 3.359 0.748 76.070 -0.089 0.386 1.000 3 5.000 15 i32 3.930 0.745 89.411 -0.347 -0.106 2.000 9 5.000 61 i33 3.666 0.704 88.163 -0.210 -0.084 2.000 13 5.000 26 i34 4.237 0.603 119.095 -0.927 4.620 1.000 2 5.000 88 i36 4.010 0.722 94.139 -0.634 1.018 1.000 1 5.000 66 i37 3.178 0.806 66.766 -0.133 -0.045 1.000 5 5.000 10 i38 3.951 0.595 112.464 -0.787 2.982 1.000 1 5.000 37 i39 3.592 0.589 103.281 -0.312 0.479 1.000 1 5.000 9 i41 3.812 0.689 93.701 -0.833 1.883 1.000 2 5.000 31 i42 3.481 0.775 76.119 -0.299 0.066 1.000 2 5.000 19 i43 4.206 0.600 118.796 -0.407 0.968 2.000 3 5.000 84 i44 4.038 0.777 88.071 -0.968 1.668 1.000 2 5.000 74 i45 4.063 0.692 99.491 -0.338 -0.058 2.000 4 5.000 74 i46 4.052 0.739 92.888 -0.659 0.873 1.000 1 5.000 76 i48 4.122 0.706 98.848 -1.016 2.700 1.000 2 5.000 79 i49 3.686 0.766 81.538 -0.433 0.463 1.000 2 5.000 33 i52 3.746 0.627 101.206 -0.435 0.460 2.000 8 5.000 21 i54 3.892 0.719 91.765 -0.577 0.912 1.000 1 5.000 48 i56 3.585 0.723 83.967 -0.971 1.441 1.000 4 5.000 12 i57 3.578 0.811 74.774 -0.553 0.904 1.000 6 5.000 28 i58 3.199 0.770 70.351 0.059 0.157 1.000 3 5.000 12 i59 3.787 0.542 118.370 -0.368 0.421 2.000 2 5.000 16 i60 3.885 0.722 91.122 -0.608 0.946 1.000 1 5.000 47 i61 3.547 0.727 82.668 -0.275 -0.198 2.000 22 5.000 18 i63 3.892 0.678 97.181 -1.150 2.996 1.000 2 5.000 35 i64 4.010 0.656 103.615 -0.310 0.290 2.000 4 5.000 59

273

AUSTRALIAN (N=320) MATCHED TO ITALY (N=320)



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.025 0.542 132.781 -0.693 4.108 1.000 1 5.000 46 i2 3.778 0.702 96.262 -0.044 -0.327 2.000 7 5.000 44 i4 3.659 0.708 92.468 -0.791 1.349 1.000 3 5.000 21 i5 4.100 0.573 127.960 -0.197 0.830 2.000 2 5.000 68 i6 3.272 0.794 73.671 -0.111 -0.165 1.000 3 5.000 14 i7 3.397 0.945 64.333 -0.172 -0.432 1.000 6 5.000 37 i8 3.603 0.748 86.142 -0.289 -0.181 2.000 24 5.000 27 i9 3.691 0.820 80.535 -0.232 -0.080 1.000 2 5.000 50 i10 3.341 0.867 68.914 -0.313 -0.441 1.000 4 5.000 18 i11 3.416 0.849 72.001 -0.539 -0.103 1.000 5 5.000 17 i13 3.891 0.651 106.917 -0.300 0.348 2.000 6 5.000 46 i14 3.425 0.860 71.202 -0.108 -0.275 1.000 3 5.000 31 i15 3.572 0.764 83.600 -0.498 0.721 1.000 4 5.000 25 i16 3.928 0.711 98.804 -0.737 1.639 1.000 2 5.000 56 i17 3.566 0.669 95.392 -0.558 0.395 1.000 1 5.000 12 i19 4.244 0.706 107.532 -0.815 0.891 2.000 8 5.000 120 i20 4.072 0.787 92.608 -0.633 0.318 1.000 1 5.000 99 i22 3.950 0.631 111.898 -0.788 2.868 1.000 2 5.000 47 i23 3.778 0.641 105.367 -0.408 0.466 2.000 9 5.000 29 i25 3.422 0.923 66.285 -0.190 -0.181 1.000 7 5.000 38 i26 3.503 0.788 79.542 -0.185 -0.016 1.000 2 5.000 27 i27 4.056 0.611 118.808 -0.362 0.912 2.000 4 5.000 65 i28 3.288 0.822 71.542 -0.236 -0.253 1.000 4 5.000 14 i29 4.441 0.701 113.277 -1.351 2.791 1.000 2 5.000 173 i31 3.353 0.821 73.047 -0.256 0.067 1.000 5 5.000 19 i32 3.856 0.798 86.392 -0.442 0.269 1.000 2 5.000 65 i33 3.663 0.767 85.446 -0.474 0.632 1.000 3 5.000 34 i34 4.234 0.661 114.576 -1.213 4.568 1.000 3 5.000 105 i36 3.975 0.763 93.172 -0.511 0.343 1.000 1 5.000 77 i37 3.184 0.834 68.265 -0.162 -0.384 1.000 5 5.000 11 i38 3.894 0.663 104.996 -0.658 1.533 1.000 1 5.000 44 i39 3.575 0.624 102.491 -0.248 0.354 1.000 1 5.000 13 i41 3.775 0.751 89.948 -0.634 0.632 1.000 1 5.000 40 i42 3.459 0.818 75.648 -0.266 0.120 1.000 4 5.000 26 i43 4.209 0.584 128.834 -0.354 1.036 2.000 3 5.000 92 i44 3.994 0.823 86.823 -0.702 0.378 1.000 1 5.000 87 i45 4.062 0.719 101.123 -0.399 -0.084 2.000 6 5.000 87 i46 3.987 0.734 97.156 -0.459 0.148 2.000 10 5.000 74 i48 4.112 0.690 106.592 -0.784 1.719 1.000 1 5.000 86 i49 3.656 0.792 82.563 -0.334 0.340 1.000 3 5.000 40 i52 3.722 0.629 105.833 -0.542 0.539 2.000 11 5.000 20 i54 3.825 0.752 91.038 -0.325 -0.085 2.000 14 5.000 53 i56 3.656 0.653 100.101 -0.459 0.218 2.000 14 5.000 18 i57 3.600 0.797 80.768 -0.125 -0.224 1.000 1 5.000 38 i58 2.978 0.797 66.808 -0.035 0.084 1.000 9 5.000 7 i59 3.772 0.572 117.974 -0.469 1.336 1.000 1 5.000 20 i60 3.822 0.706 96.885 -0.326 0.122 2.000 11 5.000 45 i61 3.491 0.772 80.915 -0.483 0.036 1.000 2 5.000 17 i63 3.975 0.623 114.142 -0.688 1.770 2.000 9 5.000 49 i64 3.900 0.740 94.246 -0.445 0.169 2.000 13 5.000 60

274

AUSTRALIAN (N=290) MATCHED TO SOUTH AFRICA WHITE (N=290)



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.014 0.532 128.364 -0.124 1.108 2.000 1 5.000 42 i2 3.821 0.717 90.713 -0.287 0.002 2.000 10 5.000 43 i4 3.690 0.802 78.306 -0.675 0.680 1.000 3 5.000 33 i5 4.059 0.623 111.023 -0.386 0.849 2.000 4 5.000 61 i6 3.255 0.822 67.464 -0.315 0.377 1.000 8 5.000 13 i7 3.490 0.908 65.421 -0.290 -0.425 1.000 3 5.000 33 i8 3.590 0.730 83.696 -0.368 0.189 1.000 1 5.000 21 i9 3.655 0.851 73.105 -0.256 -0.344 1.000 1 5.000 44 i10 3.341 0.898 63.344 -0.237 -0.626 1.000 3 5.000 20 i11 3.348 0.827 68.918 -0.504 0.097 1.000 6 5.000 12 i13 3.893 0.633 104.786 -0.406 0.725 2.000 6 5.000 38 i14 3.476 0.845 70.053 -0.461 0.189 1.000 5 5.000 23 i15 3.600 0.757 80.988 -0.632 0.752 1.000 3 5.000 21 i16 3.872 0.749 88.003 -0.781 1.349 1.000 2 5.000 47 i17 3.548 0.710 85.065 -0.667 0.609 1.000 2 5.000 12 i19 4.245 0.729 99.149 -1.008 1.726 1.000 1 5.000 111 i20 4.052 0.772 89.380 -0.771 0.901 1.000 1 5.000 80 i22 3.886 0.733 90.295 -0.934 2.145 1.000 3 5.000 45 i23 3.772 0.663 96.917 -0.286 0.190 2.000 8 5.000 30 i25 3.400 0.868 66.726 -0.169 -0.026 1.000 5 5.000 27 i26 3.559 0.784 77.326 -0.261 -0.110 1.000 1 5.000 26 i27 3.972 0.700 96.582 -0.936 2.417 1.000 2 5.000 53 i28 3.324 0.827 68.471 -0.222 -0.287 1.000 3 5.000 15 i29 4.400 0.748 100.203 -1.354 2.453 1.000 2 5.000 153 i31 3.307 0.856 65.799 0.032 0.062 1.000 5 5.000 25 i32 3.814 0.815 79.692 -0.418 -0.009 1.000 1 5.000 55 i33 3.707 0.721 87.597 -0.333 0.343 1.000 1 5.000 31 i34 4.228 0.663 108.611 -1.006 3.306 1.000 2 5.000 96 i36 3.993 0.776 87.640 -0.794 1.443 1.000 3 5.000 71 i37 3.197 0.903 60.264 -0.030 -0.522 1.000 5 5.000 18 i38 3.900 0.686 96.780 -0.840 2.188 1.000 2 5.000 42 i39 3.552 0.680 88.917 -0.288 0.275 1.000 1 5.000 15 i41 3.783 0.752 85.693 -0.997 1.753 1.000 3 5.000 32 i42 3.541 0.739 81.562 -0.324 0.346 1.000 2 5.000 20 i43 4.262 0.583 124.587 -0.217 0.071 2.000 1 5.000 96 i44 4.014 0.840 81.367 -0.908 0.943 1.000 2 5.000 81 i45 4.062 0.688 100.513 -0.466 0.396 2.000 6 5.000 72 i46 3.952 0.779 86.400 -0.712 0.990 1.000 2 5.000 65 i48 4.134 0.690 101.980 -0.882 2.080 1.000 1 5.000 81 i49 3.590 0.807 75.758 -0.393 0.479 1.000 4 5.000 31 i52 3.738 0.650 97.938 -0.518 0.523 2.000 11 5.000 22 i54 3.731 0.800 79.409 -0.375 -0.196 2.000 22 5.000 42 i56 3.607 0.709 86.660 -0.442 0.012 2.000 20 5.000 18 i57 3.579 0.821 74.266 -0.408 0.373 1.000 4 5.000 31 i58 2.959 0.831 60.640 0.151 -0.288 1.000 6 5.000 8 i59 3.728 0.627 101.302 -0.487 1.033 1.000 1 5.000 20 i60 3.790 0.735 87.791 -0.331 0.008 2.000 13 5.000 41 i61 3.476 0.772 76.653 -0.373 -0.182 1.000 1 5.000 16 i63 3.848 0.664 98.732 -0.680 1.076 2.000 12 5.000 33 i64 3.845 0.739 88.547 -0.313 -0.060 2.000 11 5.000 49

275

AUSTRALIAN (N=337) MATCHED TO SOUTH AFRICA NON WHITE (N=337)



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.006 0.506 145.368 -0.267 2.074 2.000 2 5.000 42 i2 3.760 0.747 92.384 -0.223 -0.197 2.000 15 5.000 48 i4 3.671 0.772 87.243 -0.409 0.308 1.000 2 5.000 38 i5 4.095 0.605 124.265 -0.288 0.670 2.000 3 5.000 76 i6 3.270 0.870 68.985 -0.390 0.241 1.000 12 5.000 18 i7 3.457 0.889 71.375 -0.317 -0.097 1.000 6 5.000 34 i8 3.599 0.709 93.191 -0.507 0.318 1.000 1 5.000 20 i9 3.721 0.838 81.545 -0.353 0.225 1.000 4 5.000 58 i10 3.409 0.892 70.149 -0.243 -0.637 1.000 2 5.000 28 i11 3.383 0.794 78.249 -0.365 -0.148 1.000 3 5.000 15 i13 3.955 0.628 115.705 -0.258 0.404 2.000 4 5.000 55 i14 3.475 0.849 75.157 -0.259 -0.214 1.000 3 5.000 31 i15 3.561 0.781 83.687 -0.448 0.317 1.000 3 5.000 27 i16 3.908 0.748 95.872 -0.963 1.725 1.000 2 5.000 56 i17 3.564 0.670 97.630 -0.414 0.309 1.000 1 5.000 15 i19 4.255 0.699 111.792 -0.765 1.008 1.000 1 5.000 130 i20 4.071 0.745 100.372 -0.507 0.031 2.000 9 5.000 97 i22 3.920 0.683 105.326 -1.025 2.952 1.000 3 5.000 49 i23 3.774 0.638 108.568 -0.248 0.186 2.000 7 5.000 32 i25 3.407 0.861 72.599 -0.186 0.039 1.000 6 5.000 31 i26 3.501 0.768 83.682 -0.461 0.267 1.000 3 5.000 20 i27 4.047 0.653 113.799 -0.822 2.384 1.000 1 5.000 69 i28 3.344 0.831 73.870 -0.216 -0.339 1.000 3 5.000 19 i29 4.350 0.745 107.136 -1.190 1.960 1.000 2 5.000 163 i31 3.291 0.771 78.400 0.037 -0.055 1.000 2 5.000 17 i32 3.798 0.780 89.424 -0.389 0.103 1.000 1 5.000 56 i33 3.647 0.718 93.306 -0.332 0.249 1.000 1 5.000 29 i34 4.237 0.648 120.077 -0.935 3.164 1.000 2 5.000 112 i36 3.923 0.794 90.662 -0.614 0.403 1.000 1 5.000 74 i37 3.163 0.869 66.784 -0.077 -0.328 1.000 7 5.000 16 i38 3.938 0.626 115.464 -0.467 1.004 2.000 7 5.000 49 i39 3.540 0.663 98.015 -0.271 0.232 1.000 1 5.000 15 i41 3.745 0.813 84.564 -0.872 1.025 1.000 4 5.000 42 i42 3.469 0.802 79.436 -0.421 0.177 1.000 4 5.000 22 i43 4.261 0.595 131.431 -0.415 0.795 2.000 3 5.000 112 i44 3.985 0.840 87.121 -0.730 0.630 1.000 3 5.000 94 i45 4.009 0.709 103.777 -0.365 0.022 2.000 7 5.000 79 i46 3.985 0.730 100.252 -0.486 0.510 1.000 1 5.000 77 i48 4.065 0.712 104.745 -0.791 1.495 1.000 1 5.000 84 i49 3.540 0.786 82.655 -0.281 0.211 1.000 3 5.000 30 i52 3.718 0.655 104.182 -0.527 0.465 2.000 14 5.000 24 i54 3.700 0.792 85.774 -0.352 -0.203 2.000 26 5.000 44 i56 3.629 0.687 96.957 -0.584 0.205 2.000 22 5.000 18 i57 3.493 0.791 81.055 -0.175 0.124 1.000 3 5.000 29 i58 2.914 0.857 62.453 0.052 -0.045 1.000 14 5.000 10 i59 3.772 0.586 118.137 -0.266 0.250 2.000 4 5.000 24 i60 3.730 0.704 97.292 -0.288 0.038 2.000 14 5.000 36 i61 3.469 0.820 77.658 -0.405 -0.256 1.000 2 5.000 22 i63 3.914 0.651 110.328 -0.499 0.861 2.000 9 5.000 49 i64 3.875 0.750 94.915 -0.347 -0.070 2.000 13 5.000 63

276

AUSTRALIAN (N=587) MATCHED TO SRI LANKA (N=587)



Variable Mean St. Dev. T-Value Skewness Kurtosis Minimum Freq. Maximum Freq. -------- ---- -------- ------- -------- -------- ------- ----- ------- ----- i1 4.010 0.526 184.825 -0.271 1.744 2.000 4 5.000 80 i2 3.727 0.784 115.157 -0.161 -0.287 1.000 1 5.000 91 i4 3.733 0.761 118.896 -0.373 0.189 1.000 2 5.000 77 i5 4.070 0.593 166.252 -0.217 0.584 2.000 4 5.000 121 i6 3.278 0.816 97.375 -0.380 0.402 1.000 16 5.000 25 i7 3.448 0.914 91.435 -0.263 -0.224 1.000 11 5.000 66 i8 3.598 0.733 118.874 -0.407 0.051 1.000 1 5.000 42 i9 3.768 0.816 111.843 -0.325 -0.147 1.000 2 5.000 104 i10 3.373 0.858 95.259 -0.375 -0.458 1.000 6 5.000 32 i11 3.455 0.811 103.194 -0.489 0.348 1.000 10 5.000 37 i13 3.903 0.660 143.215 -0.286 0.467 1.000 1 5.000 92 i14 3.450 0.838 99.768 -0.286 -0.230 1.000 5 5.000 46 i15 3.496 0.780 108.551 -0.408 0.258 1.000 6 5.000 39 i16 3.879 0.749 125.540 -0.803 1.249 1.000 3 5.000 95 i17 3.574 0.703 123.195 -0.576 0.676 1.000 4 5.000 30 i19 4.261 0.754 136.912 -1.070 1.710 1.000 3 5.000 241 i20 4.068 0.770 128.035 -0.567 0.153 1.000 1 5.000 176 i22 3.911 0.681 139.252 -0.606 1.290 1.000 2 5.000 92 i23 3.806 0.640 144.123 -0.235 0.184 2.000 11 5.000 63 i25 3.392 0.897 91.577 -0.171 -0.117 1.000 12 5.000 60 i26 3.523 0.791 107.939 -0.491 0.364 1.000 7 5.000 42 i27 4.017 0.676 143.961 -0.719 1.742 1.000 2 5.000 120 i28 3.310 0.828 96.877 -0.287 -0.230 1.000 8 5.000 27 i29 4.325 0.760 137.812 -1.160 1.703 1.000 3 5.000 276 i31 3.348 0.852 95.223 -0.149 0.058 1.000 11 5.000 46 i32 3.838 0.790 117.726 -0.413 0.200 1.000 3 5.000 112 i33 3.671 0.731 121.599 -0.371 0.280 1.000 2 5.000 57 i34 4.203 0.594 171.374 -0.389 1.324 1.000 1 5.000 170 i36 3.922 0.789 120.490 -0.657 0.871 1.000 5 5.000 128 i37 3.196 0.869 89.109 -0.265 -0.373 1.000 14 5.000 22 i38 3.923 0.666 142.828 -0.784 2.121 1.000 3 5.000 87 i39 3.576 0.665 130.220 -0.280 0.120 1.000 1 5.000 30 i41 3.751 0.805 112.911 -0.761 0.756 1.000 5 5.000 77 i42 3.463 0.790 106.181 -0.420 0.229 1.000 7 5.000 36 i43 4.244 0.599 171.682 -0.343 0.444 2.000 4 5.000 190 i44 3.838 0.854 108.858 -0.590 0.275 1.000 5 5.000 124 i45 4.012 0.719 135.195 -0.432 0.283 1.000 1 5.000 141 i46 4.003 0.788 123.070 -0.699 0.929 1.000 5 5.000 155 i48 4.049 0.671 146.289 -0.739 1.884 1.000 2 5.000 129 i49 3.649 0.813 108.692 -0.231 -0.121 1.000 3 5.000 80 i52 3.744 0.658 137.887 -0.540 0.760 1.000 1 5.000 48 i54 3.758 0.838 108.635 -0.340 -0.394 2.000 47 5.000 105 i56 3.579 0.714 121.387 -0.562 0.303 1.000 2 5.000 31 i57 3.578 0.809 107.182 -0.194 -0.043 1.000 4 5.000 67 i58 2.990 0.797 90.922 0.039 0.170 1.000 15 5.000 16 i59 3.763 0.587 155.259 -0.403 0.812 1.000 1 5.000 39 i60 3.809 0.740 124.767 -0.289 0.181 1.000 2 5.000 93 i61 3.491 0.767 110.268 -0.595 -0.038 1.000 3 5.000 25 i63 3.865 0.630 148.722 -0.753 1.748 1.000 1 5.000 62 i64 3.850 0.765 121.959 -0.428 0.140 1.000 1 5.000 105

277

APPENDIX 3

Descriptive statistics per subscale for the different SUEIT measurement models

This appendix includes the means, standard deviations and internal reliabilities per subscale

for the five and nine factor SUEIT measurement models fitted to each sample separately.

AUSTRALIAN SAMPLES A (N=1604) AND B (N=1605)


Scale M SD α* N of

Items

M SD α* N of

Items

Emotional Recognition and

Expression

39.41 5.13 0.81 11 39.70 5.02 0.80 11


Emotions Direct Cognition 35.08 6.56 0.86 12 35.20 6.34 0.85 12

Emotional Management 43.20 5.49 0.84 12 43.39 5.40 0.83 12


Total EI 228.97 20.45 0.72 5 228.97 20.45 0.75 5


Scale M SD α* N of

Items

M SD α* N of

Items

Emotional Recognition 7.93 1.12 0.60 2 7.99 1.11 0.54 2

Personal Expression 18.09 3.15 0.83 5 18.19 3.07 0.81 5

Others Perception 13.39 2.53 0.77 4 13.51 2.39 0.73 4


Emotions Direct Cognition negative 14.73 4.02 0.86 6 14.66 3.94 0.85 6

Emotions Direct Cognition positive 20.36 3.34 0.72 6 20.54 3.33 0.71 6

Emotional Management Others 32.58 3.91 0.77 9 32.74 3.95 0.76 9


Emotional Management Self 37.35 5.08 0.87 10 37.32 5.06 0.87 10

Total EI 228.97 20.45 0.74 7 230.08 20.52 0.76 7

Table 3.1

Five factor structure: subscale internal reliabilities, means and standard deviations for Australian split samples

*Alphas calculated after missing values were imputed


Table 3.2

Nine factor structure: subscale internal reliabilities, means and standard deviations for Australian split samples

278

NEW ZEALAND (N=234)

New Zealand (n=234)


Emotional Recognition and Expression 40.14 4.51 0.76 11



Emotional Management 43.35 5.12 0.83 12


Total EI 232.27 18.07 0.70 5

Italy (n=320)


Emotional Recognition 8.08 1.08 .57 2

Personal Expression 18.52 2.82 .78 5

Others Perception 13.54 2.29 .72 4

Understanding Emotions External 71.21 6.39 .87 18

Emotions Direct Cognition negative 15.35 3.79 .87 6

Emotions Direct Cognition positive 21.04 3.02 .68 6

Emotional Management Others 32.63 3.74 .75 9

Emotional Control 14.35 2.23 .74 4

Emotional Management Self 37.56 4.62 .85 10

Total EI 232.27 18.08 .72 7

Table 3.3

Five factor structure: subscale internal reliabilities, means and standard deviations for New-Zealand sample (n=234)


Table 3.4

Nine factor structure: subscale internal reliabilities, means and standard deviations for NZ sample (n=234)


279

UNITED STATES OF AMERICA

USA (n=287)







Total EI 232.42 24.52 0.77 5

Italy (n=320)











Total EI 232.42 24.52 .767 7

Table 3.5

Five factor structure: subscale internal reliabilities, means and standard deviations for USA sample (n=287)


Table 3.6

Nine factor structure: subscale internal reliabilities, means and standard deviations for USA sample (n=287)


280

ITALIAN

Italy (n=320)







Total EI 211.14 19.98 0.59 5

Italy (n=320)











Total EI 211.14 19.98 .60 7

Table 3.7

Five factor structure: subscale internal reliabilities, means and standard deviations for Italian sample (n=320)


Table 3.8

Nine factor structure: subscale internal reliabilities, means and standard deviations for Italian sample (n=320)


281

SOUTH AFRICA (WHITE)

South African White sample (n=290)


Emotional Recognition and Expression 36.65 5.43 .76 11


Emotions Direct Cognition 33.29 6.78 .81 12

Emotional Management 40.62 5.72 .78 12


Total EI 216.32 20.22 .59 5

Sri-Lanka (n=592)











Total EI 216.3276 20.20722 609 7

Table 3.9

Five factor structure: subscale internal reliabilities, means and standard deviations for South African White sample (n=290)


Table 3.10

Nine factor structure: subscale internal reliabilities, means and standard deviations for South African White sample (n=290)


282

SOUTH AFRICA (NON-WHITE)

South African Non-White sample (n=337)


Emotional Recognition and Expression 34.54 6.32 .74 11


Emotions Direct Cognition 33.69 6.19 .65 12

Emotional Management 40.79 7.02 .76 12


Total EI 211.12 25.29 .70 5

Sri-Lanka (n=338)











Total EI 211.02 25.29 .71 7

Table 3.11

Five factor structure: subscale internal reliabilities, means and standard deviations for South African Non-White sample (n=337)


Table 3.12

Nine factor structure: subscale internal reliabilities, means and standard deviations for South African Non-White sample (n=337)


283

SRI LANKA

Sri-Lanka (n=592)







Total EI 219.38 17.48 0.58 5

Sri-Lanka (n=592)



Personal Expression 17.59 3.03 0.69 5

Others Perception 12.99 2.67 0.73 4


Emotions Direct Cognition negative 13.47 3.52 0.69 6

Emotions Direct Cognition positive 19.81 2.81 0.46 6




Total EI 219.41 17.47 0.60 7

Table 3.13

Five factor structure: subscale internal reliabilities, means and standard deviations for Sri-Lanka sample (n=587)


Table 3.14

Nine factor structure: subscale internal reliabilities, means and standard deviations for Sri-Lanka sample (n=587)


284

APPENDIX 4

SUEIT Tucker’s Phi results

This appendix contains the results of the Tucker’s Phi analyses that were conducted

separately for every SUIET subscale (based on measurement model M2b – that is five

subscales) and country (culture), with Australia as the reference group.

A two-step procedure was used to examine construct bias which is based on exploratory

factor analysis. In the first step the covariance matrices of the two cultural groups were

combined (weighted by sample size) in order to create a single, pooled data matrix.

Factor(s) derived from this pooled covariance matrix define the global solution, with which

the factor(s) obtained in the separate cultural groups were compared (after target rotation to

the pooled solution). The agreement was evaluated by means of the factor congruence

coefficient, Tucker’s phi (Van de Vijver & Leung, 1997).

AUSTRALIA AND NEW ZEALAND

SUEIT subscale

Tucker’s phi

Australia (n=234) New-Zealand (n=234)

Emotional Expression 0.998 0.997

Understanding Emotions External 0.997 0.996

Emotional Management Self 0.999 0.999

Emotional Management Others 0.999 0.999

Emotional Control 0.999 0.998

AUSTRALIA AND USA

SUEIT subscale

Tucker’s phi

Australia (n=287) USA (n=287)






Table 4.1

Tucker’s phi results for Australia and New Zealand

Table 4.2

Tucker’s phi results for Australia and USA

285

AUSTRALIA AND ITALY

SUEIT subscale

Tucker’s phi

Australia (n=320) Italy (n=320)



Emotional Management Self 0.968 -0.908



AUSTRALIA AND SA WHITE

SUEIT subscale

Tucker’s phi

Australia (n=290) SA White (n=290)






AUSTRALIA AND SA NON-WHITE

SUEIT subscale

Tucker’s phi

Australia (n=337) SA Non-White (n=337)






Table 4.3

Tucker’s phi results for Australia and Italy

Table 4.4

Tucker’s phi results for Australia and SA White

Table 4.5

Tucker’s phi results for Australia and SA Non-White

286

AUSTRALIA AND SRI LANKA

SUEIT subscale

Tucker’s phi

Australia (n=587) SA Non-White (n=587)






Table 4.6

Tucker’s phi results for Australia and Sri-Lanka

Documents

Exploring the emotional intelligence construct: A cross-cultural investigation