VALIDATION OF THE EMOTIONAL STABILITY SCALE OF THE SOUTH AFRICAN

PERSONALITY INVENTORY

by

Farren Morgan Cohen

MINOR DISSERTATION

Submitted in the fulfillment of the requirements of the degree

MAGISTER PHILOSOPHIAE

in

INDUSTRIAL PSYCHOLOGY

in the

FACULTY OF MANAGEMENT

at the

UNIVERSITY OF JOHANNESBURG

Supervisor: Prof. Deon de Bruin

Co-Supervisor: Dr. Carin Hill

April 2013

ii

FACULTY OF MANAGEMENT

DECLARATION OF THE CORRECTNESS OF THE FINAL SUBMISSION OF

ELECTRONIC COPY OF MINOR DISSERATION/DISSERTATION/THESIS AFTER

EXAMINATION

DECLARATION BY STUDENT

STUDENT NUMBER ID NUMBER

I, Miss Farren Morgan Cohen hereby declare that the digital/electronic format of the minor

dissertation/dissertation/thesis submitted for the degree M Phil Industrial Psychology is a true

reflection of the examined minor dissertation/dissertation/thesis and incorporates any corrections

that may have been requested by the examiners. The printed and electronic copies correspond in

all respects. I am aware that the making of a false statement could render me liable for criminal

prosecution.

SIGNATURE OF STUDENT DATE

iii

ACKNOWLEDGEMENTS

I would like to thank the following people without whom this dissertation would not have been

possible:

My mother, Elaine, and father, Frank, for providing me with this opportunity. Your patience and

wisdom guided me through this journey. Your unconditional support will forever be

remembered.

To my sisters, Cara and Tia, for their support and encouragement throughout my studies.

To my boyfriend, Robin. Words cannot express my gratitude for all your support, encouragement

and unwavering belief in me. I am truly blessed to have you in my life.

My supervisor, Prof. Deon de Bruin, for affording me the opportunity to be part of such a

significant project. Thank you for your patience, guidance and support but most importantly your

belief in my ability.

My co-supervisor, Dr Hill, for your advice and support.

My mentor, Caron Hall, for your guidance throughout my studies and for the numerous

opportunities you have provided.

All collaborators of the South African Personality Inventory (SAPI) project for their time, ideas

and constructive input.

The participants of the study without whom this research would be incomplete.

The University of Johannesburg for the merit bursary and supervisor-linked bursary.

The library staff, namely Joyce Makhubedu, Timothy Valoyi and Daniel Moloisane, for their

continuous facilitation throughout my studies.

iv

ABSTRACT

The equivalent cross-cultural assessment of personality has long been a debatable subject in

psychological research. Personologists remain divided as to the universality of personality traits,

and as such, their cross-cultural applicability. This argument remains valid within the South

African multicultural and multilingual context. In addition to the applicability of various

imported personality measures, South Africa’s past misuse of psychological assessments for

unfair discriminatory purposes has created many negative perceptions of their utility. This was

further corroborated with the promulgation of the Employment Equity Act 55 of 1998 that

stipulates that all psychological assessments used in South Africa need to meet the criteria of: a)

being scientifically shown to be valid and reliable; b) can be applied fairly to all employees; and

c) not biased against any employee or group (Government Gazette, 1998).

Currently no validated indigenous model and measure of personality exists in South Africa.

Psychological assessments are mainly imported from the United States of America (US) and

United Kingdom (UK) and normed to the South African population. Foxcroft, Roodt and

Abrahams (2005) acknowledge that many of these assessments, in addition to many locally

developed measures, have not been tested for bias nor have they been cross-culturally validated.

Furthermore, the theories, models and taxonomies on which these measures are based were

developed within a Western context and as such, have not incorporated the unique intricacies of

the South African context and its array of cultures and languages. Therefore, the accurate and

appropriate measure of personality within South Africa has been impeded.

v

The present study aimed to determine the structural and item equivalence of the Emotional

Stability scale of the SAPI (South African Personality Inventory)1, a project commenced in 2006

to create an indigenous model and measure of personality in all the 11 official languages. The

Emotional Stability (ES) cluster is comprised of six sub-facets namely: Balance; Courage; Ego

Strength; Emotional Control; Emotional Sensitivity, and Neuroticism. The scale further contains

33 items.

The complete SAPI was administered to 891 participants from various organisations and

industries across South Africa. Responses to the ES scale were isolated, on which several

statistical analyses were performed. Factor retention criteria aided in determining that five

factors be extracted. Three comparison groups were created to assess the psychometric properties

of the indigenous ES scale across the following language groups: Germanic (English and

Afrikaans), Nguni (Zulu, Xhosa, Swati and Ndebele), and Sotho (Sepedi, Sesotho and Setswana).

Factor equivalence was demonstrated across all language groups assessed, indicating that ES has

the same psychological meaning across the different language groups. Tuckers’ phi attained for

ES for each language group was Germanic (pxy = 0.99), Nguni (pxy = 10.96), Sotho (pxy = 0.98).

The congruence coefficients for the group factors across the language groups did not fare as well

as the general factor. Only one group factor was above the cut-off for the Nguni group, while no

1 “The SAPI, an acronym for South African Personality Inventory, is a project that aims to develop an indigenous

personality measure for all 11 official language groups in South Africa. Participants are Byron Adams (University of

Johannesburg), Deon de Bruin (University of Johannesburg), Leon Jackson (North-West University), Carin Hill

(University of Johannesburg), Deon Meiring (University of Pretoria), Alewyn Nel (North-West University), Michael

Temane (North-West University), Velichko Valchev (Tilburg University, the Netherlands), and Fons van de Vijver

(North-West University and Tilburg University, the Netherlands).”

vi

factors met this criterion for the Sotho group. There was an exception for the Germanic group,

which had all five group factors > 0.90, indicating strong agreement.

A Schmid-Leiman transformation indicated that only 14 of the 33 items of the ES scale loaded

robustly on the general factor. This led to the culling of items which did not load on the general

factor, resulting in a reduced ES scale.

Factor retention criteria utilised indicated that three factors should be retained in the reduced ES

scale. As with the whole ES scale, the three language groups were compared. The congruence

coefficients of the general factor for the language groups were: Germanic = 0.99; Nguni = 0.95;

and Sotho = 0.96. The Tucker’s phi for the group factors did not improve in comparison to the

whole ES scale with all group factors showing agreement for the Germanic group, no group

factorial agreement for the Nguni group, and only one factor in the Sotho group displaying good

agreement.

Schmid-Leiman transformations revealed that all 14 items of the reduced scale loaded robustly

on the general factor. Furthermore, the reliability of the reduced ES scale (0.81) remained above

the cut-off of 0.70 (Nunnally & Bernstein, 1994).

A conditional anova was later performed on the reduced scale, indicating the presence of

uniform Differential Item Functioning (DIF), and a sparing amount of non-uniform DIF. Lastly,

differential item and test analyses were conducted on all the respective language groups in

comparison to one another. The results revealed that three items were biased in the comparison

of the Germanic and Nguni language groups and the Germanic and the Sotho-speaking groups.

On the contrary neither DIF nor Differential Test Functioning (DTF) was observed between the

Nguni and Sotho language groups.

vii

The present study is the first to assess Emotional Stability as it manifest indigenously in the

SAPI. The theoretical construct of ES was identified in all the language groups, corroborating the

traits’ universality. The results further revealed a reliable and valid indigenous ES scale across

the three language groups. Despite the limited number of items comprising the reduced ES scale,

this study signifies the progression of indigenous personality research, contributing to the

progress towards establishing an indigenous, cross-culturally applicable personality inventory.

Key words: Personality, Neuroticism, Emotional Stability, SAPI, Traits Approach, Cultural

Psychology, Cross-cultural Psychology

viii

TABLE OF CONTENTS

Page

DECLARATION ii

ACKNOWLEDGEMENTS iii

ABSTRACT iv

LIST OF TABLES xii

LIST OF FIGURES xiv

CHAPTER 1: INTRODUCTION 1

1.1 Introduction 1

1.2 Problem statement 1

1.3 Objectives of the current study 5

1.4 Chapter overview 5

1.5 Conclusion 7

CHAPTER 2: LITERATURE STUDY 8

2.1 Introduction 8

2.2 Trait approach 8

2.3 The lexical hypothesis 9

2.4 Neuroticism versus Emotional Stability 17

2.4.1 Eysenck hierarchical model of personality 17

2.4.2 Five factor model 19

2.4.3 The Big Five 20

2.5 Cross-cultural applicability of the Five Factor Taxonomy 21

ix

2.6 Conclusion 25

CHAPTER 3: PERSONALITY AND CULTURE 26

3.1 Introduction 26

3.2 Emic and etic approaches to personality 26

3.3 Individualism versus collectivism 28

3.4 Status of personality testing in South Africa 31

3.5 Bias and equivalence 37

3.6 South African Personality Inventory 42

3.7 Emotional Stability 44

3.8 Ethical considerations 48

3.9 Conclusion 48

CHAPTER 4: METHODOLOGY 49

4.1 Introduction 49

4.2 Participants and procedures 49

4.3 Instrument 52

4.4 Statistical analyses 55

4.4.1 Data screening 55

4.4.2 Factor analysis 56

4.4.3 The Schmid-Leiman transformation 56

4.4.4 Differential item functioning and differential test functioning analysis 57

4.4.5 Differential test functioning 59

x

4.4.6 Reliability 60

4.5 Conclusion 60

CHAPTER 5: RESULTS 61

5.1 Introduction 61

5.2 Data screening 61

5.3 Deciding the number of factors to retain 63

5.4 Hierarchical factor analysis 66

5.5 Coefficient of congruence for the whole Emotional Stability scale 69

5.5.1 Group factors across language groups 70

5.6 Reduced Emotional Stability scale 77

5.6.1 Deciding the number of factors to retain 77

5.6.2. Hierarchical factor analysis 78

5.6.3 Coefficient of congruence for reduced Emotional Stability scale 81

5.7 Conditional analysis of variance of item responses across groups 87

5.8 Differential item functioning and differential test functioning analysis 89

5.9 Conclusion 95

CHAPTER 6: DISCUSSION 96

6.1 Introduction 96

6.2 Emotional Stability in South Africa 97

6.3 Emotional Stability across language groups 97

6.4 Hierarchical Schmid-Leiman factor solution 100

xi

6.5 Reliability of the reduced Emotional Stability scale 100

6.6 Differential item and test functioning 102

6.7 Limitations and suggestions for future research 103

6.8 Conclusion 105

REFERENCES 106

xii

LIST OF TABLES

Table Description Page

Table 4.1 Demographic Composition of the Sample According to Gender 50

Table 4.2 Demographic Composition of the Sample According to Racial Group 50

Table 4.3 Demographic Composition of the Sample According to Language Group 51

Table 4.4 Demographic Composition of the Sample According to

English Reading Ability 51

Table 4.5 Demographic Composition of the Sample According to Education

Level 52

Table 4.6 ES Scale items and content 53

Table 4.7 Differential Item Functioning Categorisation Scheme of Zieky (1993) 58

Table 4.8 Differential Test Functioning Categorisation Scheme of

Penfield and Algina (2006) 59

Table 5.1 Descriptive Statistic of the Emotional Stability Scale 62

Table 5.2 Criteria to Determine Factor Retention 65

Table 5.3 Hierarchical Schmid-Leiman Solution for the

Emotional Stability Scale Items 68

Table 5.4 Factor Congruence of the Collective Group and

the Germanic Language Group 72

Table 5.5 Factor Congruence of the Collective Group and

the Nguni language group 74

Table 5.6 Factor congruence of the Collective Group and

xiii

the Sotho language group 76

Table 5.7 Hierarchical Schmid Leiman solution for the

Reduced Emotional Stability Scale Items 80

Table 5.8 Factor Congruence of the Collective Group and

the Germanic Language Group 83

Table 5.9 Factor Congruence of the Collective Group and

the Nguni Language Group 85

Table 5.10 Factor Congruence of the Collective Group and

the Sotho Language Group 87

Table 5.11 Conditional Analysis of Variance Probabilities of Uniform

and Non-Uniform Differential Item Functioning 88

Table 5.12 DIF Statistics: Polytomous Items for Germanic and Nguni Groups 90

Table 5.13 DTF Statistic: Polytomous Items for Germanic and Nguni Groups 91

Table 5.14 DIF Statistic: Polytomous Items for Germanic and Sotho Groups 92

Table 5.15 DTF Statistic: Polytomous Items for Germanic and Sotho Groups 93

Table 5.16 DIF Statistic: Polytomous Items for Nguni and Sotho Groups 94

Table 5.17 DTF Statistic: Polytomous Items for Nguni and Sotho Groups 95

xiv

LIST OF FIGURES

Figure Description Page

Figure 5.1 Scree and parallel analysis plots for a component model

and a common factor model 66

Figure 5.2 Hierarchical five-factor solution for the ES scale 67

Figure 5.3 Schmid-Leiman Hierarchical Factor Solution for Germanic group 71

Figure 5.4 Schmid-Leiman Hierarchical Factor Solution for Nguni Group 73

Figure 5.5 Schmid-Leiman Hierarchical Factor Solution for Sotho Group 75

Figure 5.6 Scree and parallel analysis plots for a component model

and a common factor model 77

Figure 5.7 Hierarchical three-factor solution for the ES reduced scale 79

Figure 5.8 Schmid-Leiman Hierarchical Factor Solution for the Germanic

group reduced scale 82

Figure 5.9 Schmid-Leiman Hierarchical Factor Solution for the Nguni

group reduced scale 84

Figure 5.10 Schmid-Leiman Hierarchical Factor Solution for the Sotho

group reduced scale 86

1

Chapter 1

Introduction

1.1 Introduction

Psychological assessment is a contentious issue within the South African context due to

its past misuse. Personality inventories are similarly controversial. This controversy stems from

the use of imported measures, whose psychometric properties portray low reliability and are

inappropriate for previously disadvantaged groups (e.g., Meiring, van de Vijver, Rothmann &

Barrick, 2005). The lack of alternative, context-applicable personality assessments hinders the

progression towards an appropriate, culturally fair and equivocal assessment of the South

African populace. This chapter aims to convey the need for developing an indigenous, locally

applicable and cross-culturally valid personality inventory for the South African population, that

is equivalent across all the respective racial, cultural, and gender groups, thus adhering to the

relevant transformational policies of this multicultural country.

1.2 Problem Statement

According to Nel et al. (2012) the personality inventories administered to the South

African populace are generally developed from Western models of personality. As such, no

indigenously derived and constructed personality inventory that is cross-culturally valid within

the South African context exists. Despite the use of valid and reliable imported measures (cf. van

Eeden & Prinsloo, 1997; the Sixteen Personality Factor Inventory) and those developed locally,

albeit based on an imported model, such as the Basic Traits Inventory (Taylor & de Bruin, 2006),

the past uncritical use and abuse of psychometric measures, including personality inventories,

fostered unfavourable attitudes towards such measures and engendered a suspicion amongst

2

South Africans regarding their utility (Bedell, van Eeden & van Staden, 1999; Rothmann &

Coetzer, 2003; Foxcroft, 1997). The majority of tests that have been administered in South

Africa have been imported from the United States and the United Kingdom, and normed to the

South African population (Cheung, van de Vijver & Leong, 2011; Foxcroft, Paterson, Le Roux

& Herbst, 2004). This is known as the etic perspective or approach to personality research, which

emphasises commonalities across cultures, implying the universality of traits (Piedmont, 1998).

This is potentially problematic as the unique intricacies of the South African population and its

diverse cultures have not been taken into consideration in the development of such instruments,

thereby refuting their applicability and suitability for such a context. Church (2001) concurs,

noting the etic perspective has been criticised for not capturing cultural-specific personality

dimensions.

Studies by Abrahams and Mauer (1999) and Meiring et al. (2005) report that imported

inventories show weak structural equivalence across cultural groups, in addition to weak

reliability in indigenous African groups. Furthermore, research by van Eeden, Taylor and du Toit

(1996) and Abrahams (1997) signified the incongruity of two versions of the Sixteen Personality

Factor Questionnaire (16PF5, 16PF, and the SA92) for individuals whose first language was not

English (see also Abrahams, 2002; Prinsloo & Ebersöhn, 2002; van Eeden & Mantsha, 2007) It

has consequently been argued that the applicability of such measures would not provide an

accurate portrayal of the personality profile for the majority of South Africans. In addition, such

measures may fail to assess certain personality dimensions pertaining to a specific culture, but on

the other hand measure attributes that have different meanings within the South African context

(Nel et al., 2012).

3

South Africa’s unique contextual and historical factors have not been incorporated into

Western measures, which therefore might inaccurately and incompletely portray attributes of

South African inhabitants (Bedell et al., 1999). A holistic view of a county’s unique contextual

factors is therefore needed in order to accurately develop and validate a test in a multicultural

society. Such a personality inventory will enable the cross-cultural equivalent measure of the

various races, cultures, ethnic and language groups within the South African context, providing

an accurate and fair description on the respective traits measured, against appropriate and

applicable norm groups.

To enable the adequate and accurate capture of representative personality dimensions, the

etic perspective had been complemented by the emic perspective, which comprises both cultural

and indigenous methodologies to psychology. Personality researchers who support and utilise the

emic perspective aim to discover the manifestation of cultural-specific traits and behaviour (cf.

Ho, Peng, Lai & Chan, 2001; Piedmont, 1998). Markus and Kityama (1998) note that the emic

perspective perceives culture and personality to be reciprocally comprised and inherently

intertwined.

Heeding the call for an indigenously derived personality inventory, researchers from the

University of Johannesburg, North-West University, and Stellenbosch University originated the

creation of an indigenous model and measure of personality, namely, the South African

Personality Inventory (SAPI), in 2006. The impetus for an indigenous personality model was to

promote a better theoretical comprehension of personality as it exhibits within the multicultural

and multilingual South African context, as opposed to depending on imported personality

4

theories, models, and inventories. The selected approach to constructing the SAPI was the use of

both emic and etic elements, to enable a more in-depth understanding of the applicability of

universal personality traits, in addition to uncovering context specific traits in South Africa.

The first stage of the SAPI project entailed the collection of data from 100 individuals

from each of the 11 official language groups through the use of interviews. More than 5 000

responses were acquired and later converted into personality descriptive items. A content

analysis of the items revealed a nine cluster model of personality traits, namely, Extraversion,

Soft-heartedness, Conscientiousness, Emotional Stability, Intellect, Openness, Integrity,

Relationship Harmony, and Facilitating (Nel et al., 2012). Each cluster is further underpinned by

several sub-facets. This taxonomy was developed by means of the lexical hypothesis, utilising

laypersons’ perceptions and comprehension of personality. The project has currently moved into

its second phase, which is the cross-cultural validation of the traits comprising the inventory.

The most current study of Emotional Stability in South Africa is that of Chrystal (2012)

who examined the factor structure of the Emotional Stability cluster. Her study revealed good

evidence and support for Emotional Stability as a dimension of personality within South Africa.

The present study centers on the dimension Emotional Stability versus Neuroticism. Both

names will be used interchangeably to emphasise both the positive and negative aspects of the

trait (de Raad, 2000). Neuroticism is a pervasive trait in personality literature, and has been

shown by previous research to be universal (Piedmont, 1998). The Emotional Stability cluster of

the SAPI is comprised of six sub-clusters, namely, Balance, Courage, Ego Strength, Emotional

Control, Emotional Sensitivity, and Neuroticism. This study is the first to explore the

5

indigenously derived Emotional Stability scale of the SAPI, determining its relevance within the

South African multicultural context.

Despite being bipolar in nature, Neuroticism was identifierd as a sub-cluster of the

overarching Emotional Stability scale. The researchers remain aware of this conceptual

confusion; however will refer to Neuroticism as the polar opposite of Emotional Stability within

this research.

1.3 Objectives of the current study

The objectives of this study are to examine the structural and item equivalence of the

Emotional Stability scale across linguistic groups in South Africa, in order to contribute to the

South African Personality Inventory (SAPI), and conclusively indigenous personality theory.

The study furthermore aims to contribute to a comprehensive knowledge and understanding of

the personality trait Neuroticism across the South African multilingual and multicultural society,

with the use of the SAPI inventory (English version). Consequently, this study will form part of a

large project ultimately aiming at the development of an indigenous personality inventory for

South Africa.

1.4 Chapter overview

Chapter 1 introduces the intent and rationale for the present study. The main objectives are

conveyed.

Chapter 2 presents the conception of the trait approach to personality, describing the lexical

hypothesis as the method utilised in taxonomy development. Thereafter, the trait Emotional

6

Stability - the focal point of the present study - is defined and discussed with reference to two of

the most prominent traits models pertinent to the present study, namely, Eysenck’s Big Three

and the Big Five of the Five Factor Model (FFM). Endorsement for both models is provided,

followed by a discussion of the cross-cultural applicability of the Five Factor Taxonomy.

Chapter 3 emphasises the significance and impact of culture on personality. Two theoretical

perspectives in the research of personality - the emic and etic approaches - are explicated with

reference to the combined emic and etic approaches. Thereafter, individualism and collectivism

as culturally applicable constructs are discussed with reference as to how they may each

respectively influence behaviour. This is followed by an investigation of personality testing and

assessment within South Africa. The issues of bias and equivalence and their impact on

psychological assessments are then discussed. The SAPI project is described, followed by

reference to the Emotional Stability scale, as the focus of the present study. Lastly, the ethical

considerations of the study are conveyed.

Chapter 4 describes procedures utilised within this research project, the research participants, the

data collection technique, and the instrument used in the data analyses. Lastly, the statistical

analyses executed in the study are reported.

Chapter 5 presents the results of the exploratory factor analysis of the Emotional Stability scale.

Subsequent to the data screening, the criteria utilised in deciding the number of factors to retain

are discussed. Thereafter, the results of the hierarchical factor analysis are presented, followed by

a congruency analysis for all comparison groups. The reduced Emotional Stability scale is then

presented, followed by the statistical analyses conducted on the scale, including a hierarchical

7

factor analysis, congruence analysis across all comparison groups, a Schmid-Leiman

transformation, a conditional anova, and lastly a differential item and test analysis.

Chapter 6 discusses the main findings from the preceding chapter. The hierarchical structure of

the reduced Emotional Stability scale is presented and contrasted with the personality models

appraised in Chapter 2. Thereafter, the psychometric properties of the scale are deliberated in

relation to preceding research commenced in South Africa. Convergent and divergent results are

explored and plausible explanations offered. The findings are then related to the pertinent

postulates of the study, and the universality of Neuroticism is established. The chapter concludes

with the limitations of the study and recommendations for future research.

1.5 Conclusion

This chapter provided an overview of the impetus to develop an indigenous personality

inventory, applicable to the diverse South African population. Imported instruments are of value,

but do not provide a comprehensive reflection of the personality taxonomy relevant to and

representative of the South African populace. Due to past discriminatory use of such measures

and in the spirit of fairness, an appropriate measure is needed.

8

Chapter 2

Literature Study

2.1 Introduction

A number of personality inventories have been developed, underpinned by either a type

or trait approach to personality. This particular study makes use of the trait approach to

personality, with reference to the three most prominent taxonomies, namely Eysenck’s Big Three

(PEN) (1992), Costa and McCrae’s Five Factor Model (1992a), and Goldberg’s Big Five (1990).

Each is discussed with reference made to the trait Neuroticism, which is of particular relevance

to this study. Thereafter the cross-cultural validity of the Neuroticism trait is explored. The emic

and etic approach to personality and the current state of personality in South Africa is also

discussed. Lastly, the South African Personality Inventory project is introduced, elaborating on

its purpose and current state of progress.

2.2 Trait approach

According to McCrae and Terraccianno (2005) and Rolland (2002), Western measures

such as the 16PF, 15FQ+, and Basic Traits Inventory (BTI) are based on the trait approach to

personality. This approach currently dominates personality psychology and particularly the

cross-cultural psychological perspective (Cheung et al., 2011). In terms of this study, the trait

approach to psychology has been utilised.

According to Church (2000), Larsen and Buss (2010) and McCrae and Costa (1990),

traits can be defined as relatively enduring, stable and consistent individual differences in

cognition, emotions and behaviour. McCrae and Costa (1996), Poortinga and van Hemert (2001)

9

and Visser and du Toit (2004) additionally noted that traits are the biologically heritable, pre-

cultural, and hierarchically structured aspects that form the foundation of personality. In

addition, they can be regarded as endogenous propensities that give rise to a person's habits,

skills, beliefs, attitudes and other adaptive capacities (McCrae, 2001). This provides an impetus

to view personality as universal and cross-culturally assessable and comparable.

Larsen and Buss (2002) note that there are three approaches from which to identify the

most relevant traits, namely the lexical approach, the statistical approach, and the theoretical

approach. The lexical approach utilises natural language to capture relevant traits. Such traits are

identified with the use of criteria such as synonym frequencies and cross-cultural universality.

The statistical approach adopts statistical procedures such as factor analysis to identify relevant

traits, in addition to identifying clusters of traits that co-vary (McCrae, 2009). The theoretical

approach utilises existing theory to infer which traits are relevant (Larsen & Buss, 2002).

2.3 The lexical hypothesis

The present study focuses on the lexical approach to personality and the derived trait of

such an approach, Neuroticism. Within this study, the terms Neuroticism and Emotional Stability

(the polar opposite of Neuroticism) will be used interchangeably (Digma, 1990). The term

Neuroticism is often given priority in the tradition of personality questionnaire construction (cf.

McCrae & John, 1992). Conversely, the term Emotional Stability is given priority in the lexical

approach to personality, and is often used in organisational contexts (de Raad, 2000). In this

study, the term Neuroticism will note behaviour that is neurotic and thus problematic in nature,

whereas Emotional Stability will emphasise more positive attributes.

10

Prior to exploring the Emotional Stability trait, a discussion of the lexical hypothesis as

an approach to developing a taxonomy of personality is required. This discussion will entail a

description of the lexical hypothesis and its utility within the personality realm. Lastly, the many

criticisms of the lexical hypothesis will be explored.

The lexical hypothesis and its inherent methods are based on the premise that all aspects

of human personality which are - and continue to be - relevant, of interest, and utility, are

encoded in language (Cattell, 1943). Galton (1884) and Goldberg (1982; 1993a) note that the

most significant individual differences between personalities form part of the daily interactions

and transactions of individuals and that these are programmed into their natural language. The

more prominent a difference between individuals, the greater its occurrence in conversations

(Goldberg, 1981; 1982; 1993a; Saucier & Goldberg, 1996; 2001). The lexical approach is based

on the aggregation of adjectives or recorded-sentences which are derived from laypersons’

description of themselves or other individuals with whom they are acquainted (Block, 2010). As

such it is assumed that the lexical approach will be sure to provide an inclusive set of personality

traits (Block, 1995). This is corroborated by Ashton and Lee (2005) and Costa and McCrae

(1992b) who note that the resultant personality structure derived from the lexical approach

enables the selection of a set of personality traits that are believed to represent the major

dimensions of personality.

Ashton and Lee (2005) further argue that the lexical approach provides the foundation for

the identification of a set of personality dimensions that is cross-culturally applicable and

appropriate. This is imperative to this research project and the context in which it occurs, i.e., the

11

multicultural South African population. The reasoning behind this assertion is that personality

characteristics derived from lexical research are indigenous to the person’s own language, rather

than being imported from a different language.

Costa and McCrae (1992c) note that swift progress has been made toward consensus on

the structure of personality. The resultant Five Factor Model (FFM) has been regarded as both

essential and ample for describing the main features of personality at a global level (McCrae &

Costa, 1986). This is corroborated by McAdams and Pals (2006) who note that the five factors

are not exhaustive in their description of personality, but are representative of the highest

hierarchical level of the trait explanation. With such a structure, a comprehensive assessment of

personality is plausible argues Buss (1989) and McCrae (1989).

Block (1995) contests the above notion on the grounds of whether the factors are

sufficient to provide an accurate, in-depth, and scientific description that is as dynamic as

personality. He further adds that the universally renowned FFM - which is based on the lexical

approach to personality research - is an ideal that limits the possible, scientifically sufficient

exploration of personality, through not adhering to various criteria. Block (1995) begins by

acknowledging that there needs to be a sufficient number of constructs to provide an adequate

description of an individual’s personality that conveys the dynamics of behaviour, enabling

discriminating explanations and predictions. Secondly, the construct needs to be theory

reflecting, i.e., deduced from theory. Lastly Block (1995) believes that scientific constructs need

not be formulated for usability and understanding of the layperson.

12

The lexical approach, namely the development of the Big Five, has received both

theoretical and psychometric criticism in that it is perceived to be capriciously derived and is

thus atheoretical (Block, 1995; Block, 2010; Boyle, 2008; McAdams, 1992). John, Goldberg and

Angleitner (1984) insist that construals derived from laypersons are confused with scientific

constructs pertaining to personality and the prediction and explanation of human behaviour. This

is further corroborated by Benet and Waller (1995), McAdams (1992) and Mischel (1968; 1973)

who express reservations regarding the lexical hypothesis. They note that the argument for the

lexical approach and its ability to comprehensively and dynamically capture personality is

attractive, yet not compelling.

Sufficient discernment, perceptiveness of personality functioning is, according to Block

(1995), lacking in laypersons. Such naivety in terms of a layperson’s understanding and

comprehension of personality renders the derived information of the lexical approach to be that

of a novice. Block (1995) further expresses doubt regarding the lexical approach on two counts:

there is no guarantee that the lexical hypothesis permits the expression of scientifically essential

aspects of personality; and the use of laypersons as opposed to psychological or personality

experts to derive personality traits.

Block (2010) notes that an additional issue with the lexical approach is that laypersons

may provide socially desirable descriptions of themselves and others in addition to possibly

being defensive in their responses. Lastly such individuals may be insufficiently self-observant

and insightful, thus inhibiting the depth and authenticity of the derived traits. This is

corroborated by personologists who review the lexical approach and its derivative adjective

13

descriptions as providing surface trait descriptions of individuals as opposed to source trait

understanding of individuals. The resultant traits are further informationally untrustworthy and

lack psychological perceptiveness (Block, 2010).

Block (1995; 2010) ultimately believes that the FFM and resultant five factor taxonomy

does not provide a comprehensive set of constructs for the portrayal and understanding of

personality. He further contends the derived taxonomy may be insufficient in accurately and

robustly capturing the dynamics of personality (Block, 1995; 2010). His apprehension extends

to the argument that the single-word trait descriptions offered and understood by laypersons

significantly detracts from the scientific premise of personality. He (1995) adds that the resultant

single-word adjectives are insipid in their description of crucial features of personality and its

dynamic functioning.

Further criticism of the lexical approach pertains to its inherent methods and approach to

deriving trait taxonomies from natural language. Block (1995; 2010) continues to note that factor

analysis may not be an ideal method in deriving a taxonomy of personality. Opponents view this

hypothesis and its resultant taxonomies as being too ambiguously defined and context–dependent

to adequately function as scientific terminology (John, Angleitner & Ostendorf, 1988).

From the above, it can be argued that the FFM and the FFA, which represent the lexical

hypothesis, contravene all of Block’s criteria in that the FFM possesses five main or general

factors of personality, is developed inductively, and is derived from laypersons perceptions, thus

being understandable, accessible and comprehensible.

14

As such Block (1995) posits several suggestions that may be used to study people and

personality, namely: behavioural observations; individual differences in numerous standardised

situational contexts; personal interviews; gathering facts about persons and their lives; and the

longitudinal study of personality development.

In response to the above criticisms, Ashton and Lee (2005) published an article in

defense of the lexical hypothesis and its contribution to the study of personality. Within this

article they tackle the criticism pertaining to the theoretical and psychometric underpinning of

the lexical approach, and render such criticisms invalid.

Ashton and Lee (2005) address the various criticisms of the lexical hypothesis namely: 1)

the argument that the adjectives provided by laypersons to describe personality inadequately

explain the complexity of such traits; 2) the ambiguous meaning of the derived adjectives utilised

to describe personality limit their utility as a descriptive construct; 3) the variables from which

factors originate are those which are utilised by laypersons as opposed to experts, thus detracting

from the robust and scientific depiction and understanding of personality (Eysenck, 1993); 4) the

use of laypersons, who may not understand people or the sources of personality variation as well

as experts do; 5) the use of laypersons to observe and rate variables of behaviour results in a

factor structure that is confounded or underpinned by such laypersons’ perceptions of personality

as opposed to a model of personality itself (Western, 1996); 6) the use of the lexical approach to

personality is believed to provide only redundant explanations of personality (Block 1995;

2010); 7) factor analysis may not be an ideal method in deriving a taxonomy of personality

(Block 1995; 2010); 8) the use of the lexical approach to inductively derive personality traits.

15

In response to the first criticism, Ashton and Lee (2005) note that the lexical approach

does not intend to encompass all meaningful personality characteristics, but rather to portray

each major dimension of personality which is believed to be manifested in a large number of

correlated characteristics encoded in personality descriptive adjectives.

Regarding the second criticism, Ashton and Lee (2005) who argue that the dual meanings

of certain adjectives that Block (1995) perceives to confound the accurate portrayal of

personality were not cause for concern in the reliable identification of coherent factors in

previous lexical studies on personality structure in diverse languages.

In response to the third criticism, Ashton and Lee (2005) note that personality experts

may confound the accurate identification of personality structure, in that their preferences and

areas of expertise may lead to the over- or under-representation of various elements of

personality. They argue that by using laypersons to derive a taxonomy of the basic dimensions of

personality, the average layperson is better able to relate to such a taxonomy, which is more

tangible and understandable.

Responding to the fourth criticism, Ashton and Lee (2005) note that laypersons’ ratings

of personality structure are important as similar factor structures (cf. Goldberg, 1990) undermine

the above criticism. Furthermore, in response to the point about laypersons’ lack of

understanding of personality in comparison to experts, Ashton and Lee (2005) acknowledge this

assumption may be true, but does not detract from laypersons being accurate raters and observers

of personality.

16

In response to the fifth criticism, Ashton and Lee (2005) defend the inductive approach to

the development of personality structure, noting that similar factor structures in languages which

have diverse lexical properties have emerged (e.g., Hungarian and Korean).

Responding to the sixth criticism, Ashton and Lee (2005) note that it refers to a lack of

explanatory power, in that derived personality factors do not provide a causal explanation of the

mechanisms that underpin personality variation. Lexical researchers do not claim to provide such

explanations (cf. Goldberg, 1993b), as such rendering this criticism invalid.

In response to the seventh criticism, the FFA provides the higher order factors of the

derived factor solution, each of which subsume numerous and different sub-facets. Its needs to

be borne in mind that the level of factor analysis would determine the number of factors derived

from the analysis. As such Block (2010) confirms the presence of five higher order factors, yet

perceives these factors and the manner in which they were derived (the lexical hypothesis) to

provide a surface level description of personality that is unscientific and limiting in its ability to

adequately describe personality as a scientific construct.

In response to Block’s (1995) criticism of the lexical approach, and its inductively

derived traits, Ashton and Lee (2005) note that personality variables derived deductively from

personality theories and on the basis of their utility diminish confidence that all applicable

aspects of personality will be adequately represented.

As can be seen from the above discussion, the lexical hypothesis is not without praise or

criticism. It however has underpinned the development of the ES scale utilised in this research

17

project. In order to progress toward the validation of this respective scale, all relevant aspects of

the lexical approach need to borne in mind.

2.4 Neuroticism versus Emotional Stability

Neuroticism was initially measured by Woodsworth’s (1917) Personal Data Sheet to

appraise the capability of soldiers to cope with military stress (de Raad & Perugini, 2002). Since

that preliminary assessment, Neuroticism has been extensively researched, being proven to be

applicable within the educational, occupational, social, and clinical contexts (Matthews, Deary &

Whiteman 2003). Neuroticism forms part of the three main trait-based personality taxonomies,

namely Eysenck’s Big Three (PEN), Costa and McCrae’s Five Factor Model, and Goldberg’s

Big Five. Each will in turn be discussed.

2.4.1 Eysenck hierarchical model of personality

Eysenck’s taxonomy of personality is strongly rooted in biology, thus inferring that traits

are highly heritable (Eysenck, 1969; Eysenck & Eysenck, 1985; Larsen & Buss, 2010). Three

main traits make up this taxonomy, namely extroversion-introversion (E), neuroticism-emotional

stability (N), and psychoticism (P) (Eysenck Personality Questionnaire; EPQ, Eysenck &

Eysenck, 1975). The structure of such a model is hierarchical with each respective trait seen to

be at the top of its own hierarchy, subsuming more narrow traits. Thus within its own hierarchy,

the Neuroticism-Emotional Stability (N) dichotomy is the broad trait (Eysenck & Eysenck, 1975,

1985; Larsen & Buss, 2010). Under this overarching trait are narrower traits, namely anxious,

depressed, guilty feelings, low self-esteem, and tense. These narrower traits in turn subsume

18

specific actions or habitual acts, namely irrational, shy, moody, and emotional (Larsen & Buss,

2002).

Within this taxonomy, individuals scoring low on N, or who are seen to be emotionally

stable, have a lower degree of emotional arousal in response to normal stresses of everyday life.

Such individuals are further regarded as even-tempered, calm, slower to react to emotional and

stressful events, and return to their base emotional rate more quickly after a distressing event

(Larsen & Buss, 2010). Conversely, individuals with a high N are inclined to be worriers, being

frequently depressed and anxious (Eysenck & Eysenck, 1975; Eysenck & Eysenck, 1985; Larsen

& Buss, 2002). These individuals experience difficulty sleeping, in addition to a variety of

psychosomatic symptoms. Furthermore, a hallmark of a high N scorer is over-reactivity with

regard to negative events and the consequent experience of negative emotions. Thus such

individuals will take more time compared to their lower N scorers to return to their base

emotional rate after a distressing event.

In spite of the above behavioural and affective correlations, Eysenck’s model has

received criticism. Firstly there are personality traits that are heritable over and above those

provided by Eysenck. Secondly, it is argued that Eysenck’s taxonomy may not be sufficiently

exhaustive, in terms of including all relevant traits. This has been argued by personality

psychologists such as Raymond Cattell, who developed the 16 personality factor system, and

more recently by Lewis Goldberg, who developed the Big Five Model to personality, and Paul

Costa and Robert McCrae who developed the five factor model to personality (Cattell, 1943;

Costa & McCrae, 1985; 1992a; Goldberg, 1990).

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2.4.2 Five factor model

The five factor model to personality is a questionnaire approach to personality, and was

developed by Paul Costa and Robert McCrae (1985, 1992a). The model, which is based on the

trait approach to personality, is currently the most widely-used model in personality research.

The instrument, which uses a self-rating sentence item format, is called the NEO-PI-R. This

acronym stands for: the Neuroticism-Extroversion-Openness (NEO) Personality Inventory (PI)

Revised (R). The clusters measured in such an instrument include neuroticism, extroversion,

openness, agreeableness, and conscientiousness (Larsen & Buss, 2010; Piedmont, 1998).

Neuroticism (or low Emotional Stability), as it is referred to in this model, includes

sample items such as ‘I have frequent mood swings’ (Larsen & Buss, 2002). This global trait of

Neuroticism is comprised of six facets, namely, anxiety, angry hostility, depression, self-

consciousness, impulsivity, and vulnerability (Costa & McCrae, 1992; Piedmont, 1998).

Neuroticism assesses an individual’s affective adjustment versus their emotional instability.

Individuals who score high on this trait are prone to experience impractical ideas, excessive

cravings or urges, psychological anguish, and maladaptive coping responses (Costa & McCrae,

1992a; Piedmont, 1998).

The five factor model has proven replicable in studies of English trait words (cf.

Goldberg, 1981, 1990). Larsen and Buss (2002) further note that the taxonomy has been

replicated every decade since its validation, indicating its replicability over time. In addition, the

20

five factors have been replicated in different item formats and different languages (cf. Costa &

McCrae, 2003; Noller, Law & Comrey, 1988; Tsauosis, 1999).

Despite the five factor model’s contribution to the study of personality, it has further

generated some controversies, including the empirical evidence of the five factor taxonomy of

personality, the identity of the fifth factor, and the comprehensiveness of the model in terms of

its completeness and whether other trait dimensions lie beyond the five (Block, 1995, 2010;

Larsen & Buss, 2002; Pervin, 1994). As such, debate has arisen as to the comprehensive nature

of the five factor model, with many of its critics noting that relevant aspects of personality have

been excluded (Block, 1995, 2010; Larsen & Buss, 2002). Despite this, the five factor model of

personality is the most robust and replicable of the existing inventories; replicating across a

variety of languages, samples, cultures, item formats, investigators, and data sources. However,

the proponents of the five factor model do admit that stating the comprehensive nature of the

taxonomy may indeed be premature. As such, the search for additional factors, whether they be

found in specific languages, cultures, and countries is an ongoing endeavour in personality

research (Larsen & Buss, 2002).

2.4.3 The Big Five

The Big Five model to personality is psycholexical in nature, and was developed by

Lewis Goldberg (1990). The broad traits comprising the Big Five include (I) surgency or

extroversion; (II) agreeableness; (III) conscientiousness; (IV) emotional stability; and (V)

openness-intellect (de Raad, 2000; Larsen & Buss, 2010). This personality taxonomy is

measured by utilising self-ratings of single word traits adjectives (Goldberg, 1990). Such

21

adjectives include talkative, warm, organised, moody, and imaginative. According to Goldberg

(1990), the key adjectives of Emotional Stability of the Big Five taxonomy include calm,

relaxed, stable versus moody, anxious, and insecure.

It is noteworthy that despite the ordering of the traits and the names assigned to each in

the NEO-PI-R in comparison to Goldberg’s Big Five model, the underlying psychological traits

of each respective model being measured are virtually identical (Larsen & Buss, 2002; 2010).

Furthermore, the use of and convergence of the factor structure of the sentence-length item

format of the five factor model and the single trait item format of the Big Five model provide

evidence of the robustness and replicability of the five factor model. That being said, of interest

to this study is the cross-cultural validity of Neuroticism vs. Emotional Stability of the five factor

model, in terms of noting the particular trait’s replicability and appropriateness across cultures.

2.5 Cross-cultural applicability of the Five Factor Taxonomy

According to McCrae (2004) the five-factor model of personality is regarded as being a

reasonably comprehensive taxonomy of personality traits. Various researchers attest to the

universality and cross-cultural applicability of the five factor model, (McCrae, 2001; McCrae,

Costa, Del Pilar, Rolland & Parker, 1998a). In line with this, research conducted by Taylor and

de Bruin (2006) advocates that personality inventories based on trait models can yield

comparable scores across multiple cultural groups in South Africa.

The five factor theory - similarly to the trait approach to personality on which it is based -

postulates the basis of traits to be entirely biological (McCrae, 2004). However, due to the fact

that characteristic adaptations - being the psychological structures people acquire throughout life

22

such as skills attitudes, schemas, habits, knowledge and schemas – are influenced by both traits

and culture, McCrae (2004) envisages that the expression of traits will vary across cultures. This

is corroborated by Church (2000) who perceives culture to have a varying impact on the

structure, level and correlates of various traits across cultures.

In line with the above argument Laher (2008a) postulates that a model can only be truly

exhaustive, universal and cross-culturally applicable if it takes other (emic) factors into account,

including a country’s unique contextual and cultural factors. Consequently, Cheung and Leung

(1998) caution that claims of the five factor model’s universality may be premature as many

cultural-specific (emic) traits were not included in such analyses and research. Poortinga and van

Hemert (2001) note that the definition of such trait dimensions and the constellation of the five

factor structure may change as a result of the information gleaned from cross-cultural research

and data on personality. Church (2001) adds that indigenous trait inventories increase the

likelihood of identifying cultural-specific dimensions of personality to assess, unlike the Western

based imported models. In essence, the cross-cultural generalisability of such a structure and its

resulting definitions will become a chief requirement for validity in the trait-orientated approach

to personality and personality research.

Content validity is of the utmost importance when assessing personality across cultures.

Church (2001) notes that when an imported, etic measure is used to measure universal traits,

some element of the item may not tap relevant indicators of the trait in new cultural settings and

relevant emic indicators may also be missed. He further adds that such an occurrence has

implications of how accurately the trait is represented in each culture. It needs to be ascertained

23

whether the use and value of indigenous measures outweigh that of imposed etic ones. Church

(2001) states that an indigenous measure should contribute incremental validity beyond that

reported by etic, imported measures. This was found in research conducted by Cheung et al.

(2001) on the Chinese Personality Inventory (cf. Cheung et al., 1996; 2001).

Despite the above arguments pertaining to the universality of the five factor model and its

encompassing traits, Johnson (1997) advocates that traits are imperative to a systematic

understanding of personality. Wiggins (1997) further adds that without the existence of traits, the

study of personality, and consequently the psychometric approach, would not exist. This speaks

to the centrality of traits and the trait perspective in studying personality. Proponents of the trait

approach focus on the etic (universal) nature of traits which is imperative to cross-cultural

research of personality, whereas the proponents of the cultural approach to personality focus on

the emic (indigenous) nature of such traits (Chrystal, 2012). Both the above approaches are

needed in the advancement of knowledge and development in personality theory (van de Vijver

& Leung, 2001). Thus a distinction needs to be drawn between the cross-cultural and cultural

approaches to psychology and personality.

The trait perspective, as noted, plays a central role in the cross-cultural trait psychology

approach (Church, 2000). This approach acknowledges personality and culture to be mutually

exclusive, independent variables. Lonner and Adamopoulos (1997) define cross-cultural

psychology to view culture as independent and thus distinguishable from an individual’s

personality. Cross-cultural personality psychologists are interested in the etic approach to

personality in terms of the culture universality of traits and the generality of personality theories

24

and constructs, as well as illuminating cultural influences on personality and behaviour (Church,

2000).

Conversely, according to Church (2000), culture is assumed to have a varying impact on

the structure, level and correlates of various traits across cultures. Consequently, this centrality of

traits in the cross-cultural approach to psychology is questioned within the cultural psychological

approach. The latter approach proposes that culture is a fundamental part of personality, and its

inherent differences across cultures will be duly noted in their influence on an individual’s

personality and behaviour (Triandis & Suh, 2002). In line with cultural and indigenous

psychology, Church (2000) views culture as influencing the manner and extent to which traits

are expressed in different contexts.

The applicability of the trait approach to personality has been questioned in non-western

cultures (Church, 2000). This belief has been refuted in terms of research conducted by non-

western psychologists who have discovered indigenous constructs, resembling individual

differences or traits (Church, 2000). These include - but are not limited to - the Japanese traits

amae, meaning indulgent dependence, and sunao, referring to docility and peace of mind

(Murase, 1982); the Chinese trait ren qin, meaning relationship orientation (Cheung et al., 1996);

and lastly the Indian trait or concept of hishkama karma, meaning detachment (Sinha, 1993).

Church (2000) acknowledges that the feasibility of the trait approach does not necessitate

that the same traits exist across cultures. Culture-specific (emic), indigenous traits may indeed

exist. Costa and McCrae (1995) and McCrae and Costa (1996) note that traits as inherited basic

tendencies and external influences such as culture, are viewed as independent, co-determinants

25

of an individual’s personality. Consequently, Church (2000) advocates the need for more

extensive research in order to identify and assess indigenous personality traits and to ascertain

whether such traits enable the prediction of indigenous traits above that of the five factor model.

2.6 Conclusion

This chapter provided an exploration of the trait approach to personality, focusing on the

main three taxonomies with reference to Emotional Stability in each. The lexical hypothesis as

an approach to the construction of personality taxonomies was explored. Thereafter, the cross-

cultural applicability or validity the five factor taxonomy was explored in relation to its

applicability within the South African context. This chapter further highlighted the need for

widespread research to identify indigenous personality traits and document how they manifest in

behaviour that is applicable to the context in which they originate. Numerous approaches have

been suggested for cultural and cross-cultural personality research. Of the most prominent are the

etic and emic approaches to personality research and culture.

26

Chapter 3

Personality and culture

3.1 Introduction

This chapter underscores the importance and impact of culture on personality. Two

theoretical viewpoints in the research of personality, namely, the emic and etic methodologies,

are described with reference to the combined emic and etic approaches. Thereafter, individualism

and collectivism as culturally applicable constructs are discussed with reference made as to how

they may each respectively influence behaviour. This is followed by an investigation of

personality testing and assessment in South Africa. The issues of bias and equivalence - and their

impact on psychological assessments - are then discussed. The SAPI project is described,

followed by reference to the Emotional Stability scale, as the focus of the present study. Lastly,

the ethical considerations of the study are conveyed.

3.2 Emic and etic approaches to personality

The inherent need for culturally representative, applicable, and appropriate personality

models and measures has led to a combination of an etic-emic approach to indigenous

personality assessment as utilised by Cheung, van de Vijver and Leong (2011) and Net et al.

(2012). These articles illustrate the complementary utilisation of both the emic and etic

approaches to the development of indigenous personality inventories and the assessment of

personality with such instruments. The etic (cultural-comparative), top-down approach to

personality centers on the universal applicability of traits and on the establishment of

27

measurement equivalence in imported measures of personality (Cheung et al., 2011; Poortinga &

van Hemert, 2001; Nel et al., 2012). In addition, such an approach usually employs inventories

(Church, 2001). The emic (indigenous), bottom-up approach studies personality in specific

cultures, investigating the traits in that particular culture (Cheung et al., 2011; Poortinga & van

Hemert, 2001; Nel et al., 2012). Furthermore the emic approach seeks to maximize the suitability

of an instrument in the target culture (Church, 2001). The etic approach has been utilised for the

identification of common personality dimensions across cultures; whereas the emic approach

identifies unique, cultural-specific dimensions of a particular culture (Nel et al., 2012). In line

with the previous statement, Poortinga and van Hemert (2001) view cross-cultural differences

evidenced in data on personality research to be emic in nature; whereas similarities across

samples are regarded as etic in nature.

Cheung et al. (2011) propose that a combined emic-etic approach can facilitate in making

personality theory universal, while simultaneously adding cultural-specific components to

current Western models. In addition, the combined approach enables conceptual advances in the

field of personality theory and the delineating of universal and cultural specific aspects of

personality constructs (Jahoda, 1995; van de Vijver & Leung, 2001). Consequently, the use of

the emic-etic approach enables the linkage of cross-cultural, cultural and indigenous psychology,

further enabling an appreciation of which personality aspects are shared across which types of

cultures (Cheung et al., 2011; Nel et al., 2012). Poortinga and van Hemert (2001) acknowledge

that a combination of such approaches is plausible and possible when researchers concur that

different perspectives can contribute to the analysis of the same issue, in this instance,

28

personality. Additionally, Jahoda (1995) argues that the emic and etic approach do not appear to

be contradictory; rather both are needed for the advancement of knowledge.

To effectively ascertain the universal and cultural-specific traits within a country, the

context of such a country needs to be taken into consideration. The context will have an impact

not only on the values espoused by a population, but will influence the interpretation and thus the

advancement of knowledge. The context referenced may be that of an individualistic or

collectivistic environment.

3.3 Individualism versus Collectivism

Within and across cultures, distinctions can be drawn as to a culture’s propensity to be an

individualistic culture or a collectivistic culture. Church (2000) and Triandis (1989) define

individualists as being independent, perceiving the self as autonomous and focusing on

individual as opposed to group goals. These cultures or societies are further seen to be egocentric

in nature (Laher, 2008a). Collectivistic cultures, on the other hand, according to Laher (2008a)

either make no distinction between individual and group goals, or subordinate individual goals

for those of the collective group to which they belong. In addition, collectivistic cultures are seen

to be interdependent and more sociocentric in nature. Furthermore Church (2000) notes that

individualists emphasise personal attributes in guiding behaviour whereas collectivists emphasise

roles and norms in such guidance. Consequently, according to Markus and Kitayama (1998) and

Triandis (1995), the terms individualism and collectivism seem to imply that the behaviour of

persons in collectivistic cultures is less traited and therefore less cross-situationally inconsistent

compared to that of individualistic cultures.

29

Of importance is to acknowledge that individualism and collectivism do not represent

polarities but rather lie on a continuum (Church, 2000). These terms and their resultant

dimensions can and do occur across and within cultures (see Green, Deschamps & Páez, 2005).

Vogt and Laher (2009) note that it is stereotypical to assume that an individual characterised as

either an individualist or collectivist would possess all characteristics of that culture (Vogt &

Laher, 2009). Furthermore, Triandis (1989) argues that cultures vary in their emphasis on the

individual versus the collective. Markus and Kitayama (1998) further add that culture may also

differ in terms of their self-construals of an individual as being independent versus

interdependent with respective others. Hence distinctions can be drawn between varying types of

individualism and collectivism both across and within cultures (see Green, Deschamps & Páez,

2005).

South Africa is comprised of a variety of cultures which vary in terms of individualism

and collectivism (McCrae, 2004). It is noted that Germanic-speaking people, namely, English

and Afrikaans speakers, are said to be individualistic in nature, whereas African language-

speaking people are said to be collectivistic (Adams, van de Vijver & de Bruin, 2012). Allik and

McCrae (2004) add that the personality profiles of black and white South Africans are especially

different, despite the fact that such individuals have lived in the same country for generations. In

spite of this, South Africa is regarded to be collectivistic in nature. However, due to its vast

cultural diversity; no clear distinctions can be drawn. Furthermore, support exists for African

cultures being regarded as collectivistic and White cultures as individualistic (see Eaton & Louw,

2000; McCrae, 2004; Mpofu, 2001).

30

In South Africa the collectivistic dimension is best captured by the local term ‘ubuntu’

meaning that a person exists as they are as a result of their interactions with other people (Vogt

& Laher, 2009). It has been argued that ubuntu is not an absolute collectivistic dimension,

enforcing a communal identity on its inhabitants (Laher, 2008a; Louw, 2001). Rather, it

integrates discourse and endorses the effective functioning of an individual in a community,

giving precedence to that community (Laher, 2008a; Vogt & Laher, 2009).

In terms of this unique cultural factor, ‘ubuntu’ is an indigenous South African term

referring to the influence other people have on one’s development, growth and success in life

(Schutte, 2001). It further refers to the interdependent relationships among people in society.

These relationships are characterised by reverence, compassion, respect, acceptance, loyalty

patience, courtesy, generosity, hospitality and cooperativeness (Laher, 2008a). It is believed that

ubuntu as it manifests within an individual will contribute to the wholeness and integrity of that

individual’s character that is present in their decisions, feelings and judgments and which will

ultimately provide them with a sense of self-confidence, worth and dignity (Schutte, 2001).

The collectivistic value of a culture, in particular the South African culture, is expected

to emphasise the relational aspect of personality (Nel et al., 2012). Consequently, the authors

note that the five factor model would have to expand in order to accommodate attributes related

to such relational aspects of personality. Despite the focus of personality research on both

individualism and collectivism, Vogt and Laher (2009) found an insignificant relationship

between race, the five factor model and individualism/collectivism as well as between language,

the five factor model and individualism/collectivism.

31

3.4 Status of personality testing and assessment in South Africa

The previous application of Western models to various countries including South Africa

and other non-western countries, and their specific cultural context, has indicated that the

personality structure of these cultures do indeed overlap, providing evidence of the universal

structure of personality (see McCrae & Costa, 1997; McCrae, Yik, Trapnell, Bond & Paulhus,

1998b; Church & Lonner, 1998). However, according to Berry, Poortinga, Segall and Dasen,

2002) many cross-cultural variations in such a structure are evident, in addition to the structure

not being replicable across cultures (see Cheung et al., 2011; Heaven & Pretorious, 1998, Taylor,

2000). Furthermore certain dimensions relevant to a particular culture may not be assessed or

represented in such a structure. This is corroborated by Hendriks et al. (2003) who note that

imported, etic personality tests do appear to be useful tools, even though they may not fully

cover the local culture’s reality.

Laher (2008a) and Vogt and Laher (2009) note that the five factor model, as an

association of personality traits, may be applicable to the South African context even thought the

constructs may manifest in different ways across and within the array of South African cultures.

Furthermore the essence of such constructs may differ across such cultures. Consequently,

McCrae and Terracciano (2005) and Allik and McCrae (2004) acknowledge the likelihood that

there may be some African - and consequently South African - personality structures and

constructs that differ from the five factor structure. This is supported by Church (2000), Cheung

(2004), Cheung et al. (2001) McCrae and Allik (2002), McCrae and Terraccianno (2005) and

Meiring et al. (2005) who acknowledge that the five factor model may not be sufficiently

32

exhaustive, especially within African and Asian cultures. In addition Vogt and Laher (2009)

note that the five factor model has not been replicable in cross-cultural research in terms of both

the number and structure of factors. This is attributable to the fact that the five factor model does

not account for various indigenous traits of personality (Valchev, van de Vijver, Nel, Rothmann,

Meiring & de Bruin, 2011).

At the factor level of analysis, the universality of the five factor model is questioned

when emic, cultural-specific traits emerge in the personality analysis of non-western countries

(Poortinga & van Hemert, 2001). These additional, emic factors are seen to be independent of the

five factor dimensions. This provides evidence that the five factor model may be incomplete and

non-exhaustive. The uniqueness of the Chinese Personality Assessment Inventory’s (CPAI; see

Cheung et al., 1996) Interpersonal Relatedness factor as well as the uniqueness of the SAPI

Facilitating and Relationship Harmony factors, further challenge the completeness of the five

factor model (Cheung, 2004).

Laher (2008a) notes that a common criticism of the five factor model and its applicability

across different cultures may hinge on the fact that this model and its resultant instruments have

been developed from the analysis of adjective terms in the English language. As such, equivalent

translations into target languages may be hampered due to a lack of corresponding terms in such

languages. Laher (2008a) further concludes in her research that the five factor model is only

partially applicable within the South African context. The etic structure is replicable. However,

certain factors appear to load on emic factors particular to the South African context.

33

Furthermore these factors may manifest differently in the South African context, mainly across

the array of South African cultures.

The pseudo-etic approach, in terms of taking a test from one culture and using it in

another culture - either as is or adapting it - has been applied to South Africa (Laher, 2008a).

Consequently it needs to be noted that acculturation within the South African context may

further exacerbate the ineffectual administration of etic personality measures, such as the five

factor model, the 16PF, the Occupational Personality Questionnaire (OPQ), and the 15FQ+.

Acculturation is defined by Redfield, Linton and Herskovits (1936) as phenomena which result

in an individual coming into contact with various cultures, with subsequent changes in the

individual’s culture and the cultures to which they come into contact with. Furthermore as a

result of globalisation, African cultures are continuously being exposed to a variety of

international cultures, both individualistic and collectivistic.

The discrepancies regarding the universality of the five factor model, according to

McCrae (2004), area consequence of the differences between the individualistic and collectivistic

cultures in which such measures are administered. Due to the past discriminatory use of

assessments, the cross-cultural applicability of personality assessments needs to remain

cognisant of race and gender as a variable (Vogt & Laher, 2009). Furthermore, previous research

has noted the influential effects that factors such as age, gender and socio-economic status have

on personality traits above the influence of culture (Costa, Terraccianno & McCrae, 2001;

McCrae et al., 1998b). Consequently, Vogt and Laher (2009) refer to the need for further

research of the influence of the cultural factors of race and home language on personality traits

34

and varying levels and degrees of presentation across cultures. They further advocate that an

emic approach similar to that of Cheung and colleagues, would further existing knowledge of

personality in South Africa beyond that provided by the currently enlisted etic and pseudo-etic

approaches.

Ramsay, Taylor, de Bruin and Meiring (2008) acknowledge that the various language

groups in South Africa do have cultural differences. Therefore, personality measures need to be

validated for use across this array of cultures in order for such instruments to be perceived as fair

and the results accurate. Consequently any personality measure designed for the South African

context and its unique cultural dynamics should be evaluated in terms of measurement

equivalence (Ramsay et al., 2008).

The psychometric issues of weak structural equivalence and low reliability across ethnic

groups within the South African context have to an extent been attributed to language problems

in terms of participants not being assessed in their mother tongue (Nel et al., 2012). Many would

argue for the sufficiency of translating etic, imported personality tests and norming these to the

South African population (Paterson & Uys, 2005). This would prove to be problematic since,

whereas in the past item translation was regarded as a purely linguistic issue, there now exists a

growing awareness of the need for items and instruments which are translated to be cognisant of

the wider context and that translation furthermore requires expertise in the indigenous language/s

and culture/s of the specific target group as well as in item writing (Hambleton, 2001;

Hambleton, Merenda & Spielberger, 2005; van de Vijver & Hambleton, 1996). Meiring, van de

Vijver and Rothmann. (2006) acknowledge that adapted tests do have advantages in terms of

35

lower costs of production and their flexibility in dealing with major sources of bias. However,

these tests do pose a few disadvantages - most notably that their equivalence in terms of the

modified items must be demonstrated (Meiring et al., 2006). Adapted personality tests that are

applied to the South African population do in turn render a few obstacles in that the educational

level, home language and proficiency in English of the population - and resulting sample -

greatly hamper the item and construct comparability of such personality tests (Meiring et al.,

2005; Meiring et al., 2006).

Consequently, in an attempt to eradicate such issues of weak equivalence and low

reliability, Meiring et al. (2006) note that item adaptation is not a viable option in this regard.

This is corroborated by previous research that notes that it needs to be borne in mind that

language proficiency, in that language, in which the assessment is administered, does indeed play

a role in the observed above-stated differences (see Abrahams 1996, 2002; Bedell et al., 1999;

Laher, 2008a; Meiring et al., 2006; van de Vijver & Rothmann, 2004).

According to Abrahams (1996, 2002), Meiring (2000), and Taylor (2000) inadequate

research has been conducted on personality within the multicultural South African context.

Amongst the research that has been conducted, is a study by Meiring et al. (2005) who examined

the sufficiency of the fifteen factor questionnaire (15FQ+) in police candidates across South

Africa. They concluded that this instrument was not suitable for the assessment of personality

within the multicultural South African context due to a lack of internal consistency with various

constructs as well as the lack of construct equivalence. Meiring et al. (2006) further add that the

internal consistency of the items is unlikely to increase if further adaptations are made to the item

36

content. Consequently this measure failed to effectively and accurately measure personality as it

is construed and manifested within the South African context, providing further support for the

need for an indigenously-derived personality inventory. Furthermore, previous research using the

sixteen personality factor questionnaire (16PF) and its cross cultural applicability within the

South African context has found little support (Meiring et al., 2006).

Studies conducted in South Africa regarding the applicability of the five factor model to

this unique context provided conflicting results (Visser & du Toit, 2004). In research conducted

by Heaven, Connors and Stones (1994), support appeared to be lacking for the five factor

structure and its applicability to Black South African students. In addition, research conducted by

Heaven and Pretorious (1998) further illustrated the lack of support for such a model when the

traits were translated for black Sotho-speaking students. On the other hand, the five factor

structure was replicable in research conducted with an Afrikaans-speaking student sample

(Visser & du Toit, 2004). As can be deduced, the cross-cultural applicability of the five factor

model, and any personality inventory for that matter, does not yield comparable results due to

cross-cultural, economic and lingual differences among South Africa’s culturally diverse

population. This further speaks to the need for an indigenously-derived theory and model of

personality applicable to the South African context.

As noted, the use of indigenous measures will enable the identification of culture-specific

traits. This, in combination with imported measures, increases the likelihood that a more

comprehensive approach to personality can be fostered (Church, 2001). Progress has been made

in this regard with the development of the Basic Traits Inventory (BTI) by Taylor and de Bruin

37

(2006). This instrument is an English-language measure of the five factor model developed

within South Africa. It is hierarchical in nature, with five broad traits on the highest level, and 24

dimensions on the lower level (Laher, 2008a). Research on the BTI has demonstrated that the

five factors inherent in the model are replicable across South African cultures (see de Bruin,

Schepers & Taylor, 2005; Ramsay, Taylor, de Bruin & Meiring, 2005). Despite the

accomplishments of this emic approach, it still does not address the comprehensive nature of the

five factor model as an acceptable measurement of South African personality (Laher, 2008a).

Therefore, the need for an indigenously-derived, emic model is further evidenced by this

development.

Despite the above need, the past and consistent use of pseudo-etic personality measures

in South Africa resulted in a greater susceptibility of such measures, compared to that of

indigenous emic measures, to run into bias issues as they may be inadequate in tapping the

personality constructs specific to a particular culture (van de Vijver & Leung, 2001).

Consequently the issue of bias and equivalence is of great importance to the administration and

interpretation of psychometric measures in the culturally diverse South African context.

3.5 Bias and equivalence

Utilising Western models within the South African multicultural and multilinguistic

context can potentially introduce bias into the assessment process. When personality measures

are administered cross-culturally, issues pertaining to measurement bias and equivalence become

prominent, thereby affecting the comparability of constructs or scores across cultures (Cheung et

al. 2011; Church, 2001; van de Vijver & Leung, 1997). Bias is defined as the existence of

38

troublesome or systematic error in a measure (van de Vijver & Leung, 2001). This issue of bias

in cross-cultural research can be overcome through the development of indigenous, cultural-

specific measurements (van de Vijver, Hofer & Chasiotis, n.d).

According to van de Vijver and Tanzer (1997) three sources of bias exist in cross-cultural

measurement, namely, construct bias, method bias, and item bias. Church (2001) notes that such

forms of bias confound mean comparisons across cultural groups. Construct bias occurs when

the constructs measured are not identical among the various cultural groups thereby failing to

verify the equivalence of the construct across cultures (Bedell et al., 1999; Meiring et al., 2006).

Consequently, construct bias prevents the measurement of a construct with the same measuring

instrument (van de Vijver & Rothmann, 2004). The differing dimensions of the constructs

assessed may be attributed to how different cultures define, value and perceive constructs and

their respective attributes. Van de Vijver and Rothmann (2004) add that such sources of bias may

be attributable to a partial overlap of the definition of the construct across cultures; differential

appropriateness of the behaviour associated with the construct; poor sampling of the relevant

behaviour through the inadequate use of items and lastly, incomplete coverage of all the facets of

the dimension assessed.

Method bias refers to sources of bias that emanate from various methods and procedures

of research that may confound cross-cultural comparisons (van de Vijver & Rothmann, 2004).

Meiring et al. (2006) add that method bias refers to generic person-related or instrument-related

aspects that may affect the magnitude of cross-cultural score differences. There are three forms

of method bias, namely, sample bias, administration bias and instrument bias. Van de Vijver and

39

Rothmann (2004) note the various sources of method bias to be: the incomparability of samples;

differences in environmental administration circumstances; the use of ambiguous instruments

and administrative instructions; different levels of expertise of measurement administrators;

tester effects; communication difficulties between the tester and the respondent; differential

familiarity with stimulus material; differential familiarity with response procedures and lastly,

differential response styles in terms of social desirability, acquiescence, and extreme scoring.

Item bias results in different scores between participants who have an equal standing on

the underlying construct (van de Vijver & Rothmann, 2004). Such items do not function

similarly across different cultural groups with their fundamental meaning differing across the

cultures assessed (Meiring et al., 2006). Furthermore the presence of item bias indicates

incongruities at the item level across cultures (van de Vijver & Leung, 2001). The most general

sources of item bias are ambiguities relating to the item content, poor translation and adaptation

of items, nuisance factors, low familiarity and low applicability of the items to a certain culture

(Church, 2001; van de Vijver & Rothmann, 2004). Each of these different types of bias needs to

be taken into account when developing a personality scale in a multicultural context. In addition,

imported instruments need to be evaluated with respect to each of the three types of bias.

According to Poortinga and van Hemert (2001) sources of bias in cross-cultural research

may have an increasing effect on the size differences observed across cultural groups regarding

the presence and strength of the personality traits assessed. This bias is more likely to

overestimate rather than underestimate the variance attributed to cultural differences (Poortinga

& van Hemert, 2001).

40

With the adaptation of imported tests to South African society, many factors come into

play, affecting the equivalence of such measures to a particular culture. Equivalence denotes the

repercussion of bias on the comparability of constructs and test scores across cultures (van de

Vijver & Leung, 2001). The cross-cultural applicability of an instrument infers the need to

establish the equivalence of such an instrument (van de Vijver & Rothmann, 2004).

Consequently Cheung et al. (2011) advocate that if a researcher assesses personality from an

imported measure, emic (indigenous) aspects of the construct will remain hidden. To ensure

adequate adaptation of an instrument and its encompassing construct and items, extensive

pretesting and interviews will need to be conducted in order to ensure equivalence in all three

forms, namely, construct equivalence (structural equivalence), measurement unit equivalence,

and scalar equivalence (Cheung et al. 2011).

Construct equivalence is present when the same construct is measured across groups,

irrespective of the instrument used (van de Vijver, Hofer & Chasiotis, n.d). Furthermore, it is

attained when the correlations among the variables within a construct are identical in different

groups (van de Vijver & Leung, 1997, 2001). This form of equivalence, according to Poortinga

and van Hemert (2001) and Poortinga, van de Vijver and van Hemert (2002) is the minimum

prerequisite needed in order to fairly conduct cross-cultural comparisons. Furthermore, it is

paramount when conducting cross-cultural research in personality. As such, research needs to

establish the cross-cultural identity or non-identity of structures, dimensions and constructs by

means of personality questionnaires. Van de Vijver and Tanzer (2004) note that the presence of

construct equivalence implies the universal validity of the underlying psychological construct,

which in line with cross-cultural terminology, can be associated with an etic position. Conversely

41

the assumption of construct inequivalence can be associated with the emic position, which infers

the idiosyncratic nature of each culture, favouring an indigenous approach to assessment (van de

Vijver & Tanzer, 2004).

Metric equivalence or measurement unit equivalence requires that constructs not only be

identical and replicable across cultures, but that the distance between the scale points on which

the construct is measured should be identical and therefore cross-culturally comparable (van de

Vijver & Leung, 1997). Van de Vijver and Tanzer (2004) add that such equivalence is obtained

when the measurement units are the same across cultures but have different origins. Furthermore

in cross-cultural studies, if such scores are obtained, they may not be directly comparable unless

their unit of measurement is known and can be converted.

Scalar equivalence or full scale equivalence will be present when both construct and

metric equivalence are attained in addition to the origin of the scale being the same across all

groups compared (van de Vijver & Leung, 1997, 2001). Furthermore scalar equivalence requires

a score to have precisely the same meaning in terms of its interpretation across cultures.

Moreover, scalar equivalence further implies there be joint-zero point across cultures. It is only

attained when the measure is completely bias-free, that is when there is no construct, method or

item bias present (Visser & Viviers, 2010).

Van de Vijver and Tanzer (2004) advocate that a distinction between metric and scalar

equivalence is important for cross-cultural research. In addition, the psychometric analyses of the

three aforementioned forms of equivalence are imperative to the examination of cross-cultural

consistency of personality traits (Poortinga & van Hemert, 2001).Consequently, Church (2000)

42

and Ponterotto (2010) advocate the need for instruments and models that can accurately describe

the personalities of individuals in a multicultural country like South Africa. Bartram (2008)

indicates that this study will best be informed by both universal and cultural specific views of

personality. Consequently, the cross-cultural equivalence of personality instruments needs to be

established in order to assess the universal generalisability of personality dimensions (van de

Vijver & Poortinga, 1997).

3.6 South African Personality Inventory Project

The above-mentioned need for a South African-centered personality inventory provided

the impetus for the development of the South African Personality Inventory (SAPI) project,

which intends to generate an indigenous model of personality traits that applies across different

cultural/language/ethnic groups (Nel et al., 2012). Furthermore the project aims to develop a

personality inventory (the SAPI), that is culturally informed, psychometrically sound and legally

compliant (see Employment Equity Act 55 of 1998, section 8) and measures the traits of the

indigenous model (Government Gazette, 1998).

As outlined above, van de Vijver and Leung (2001) and van de Vijver and Tanzer (1997,

2004) note that imported personality measures are likely to encounter bias and consequently

equivalence problems as they may be inadequate in tapping the underlying personality constructs

outside their own culture. This issue has been addressed with the decentered instrument

developed from the SAPI initiative. The decentered approach to measurement development is

preferable considering the South African context as it is useful in identifying universally shared

aspects of personality, as well as cultural-specific aspects (van de Vijver & Leung, 2001; van de

43

Vijver & Poortinga, 1997; van de Vijver & Rothmann, 2004). Van de Vijver and Leung (2001)

and van de Vijver and Rothmann (2004) note that not only will this approach identify universally

shared aspects of personality, but it will also further identify cultural-specific personality

dimensions or traits. Larsen and Buss (2002) note that traits that emerge universally in different

cultures and languages are deemed more relevant than those that lack cross-cultural universality

when the lexical approach of constructing a personality inventory is utilised.

According to Rothmann and Coetzer (2003), researchers agree that almost all personality

measures could be categories according to the five factor model. This indeed proves to be the

case with the SAPI, with the addition of a few indigenous, culture-specific factors. Previous

research in the development of the SAPI notes that the indigenously-derived model of

personality represents data from the eleven major cultural-linguistic groups in South Africa (Nel

et al., 2012). In addition, it is duly noted that the model incorporates both facets common across

all or most groups and facets found in only a small number of groups or a single group.

Furthermore Nel et al. (2012) note that the structure of their model accommodates the core

elements of personality relevant to the South African multi-cultural-linguistic context and thus

conclude that their derived model forms a strong foundation for the assessment of personality in

South Africa.

The nine clusters present in the SAPI are: extraversion, soft-heartedness,

conscientiousness, emotional stability, intellect, openness, facilitating, integrity, and relationship

harmony. Nel et al. (2012) acknowledge that this conceptual model exhibits both similarities and

differences to the dominant five factor model. Extraversion, soft-heartedness, conscientiousness,

44

emotional stability, and intellect correspond broadly to the five factor model (Nel et al., 2012).

For the purpose of this research, Emotional Stability will be the construct of focus within the

South African context.

Assessments need to be relevant and applicable to the countries and cultures in which

they are administered. Church (2001) succinctly states that in order to study personality across

cultures, we have to accurately measure it. The trait approach and the resultant factor structure of

the SAPI provide taxonomy of independent dimensions of personality that can be classified and

explained with regards to the South African population.

3.7 Emotional stability

The trait to be measured in this research article will be that of Emotional Stability and its

applicability and utility within the South African context, as derived from the SAPI inventory.

Neuroticism is understood to be the polar opposite of Emotional Stability as indicated in the

NEO-PI-R, and will be referred to in the research as such (Costa & McCrae, 1992a). According

to the SAPI, Neuroticism falls under the cluster of Emotional Stability as one of its several

subclusters, namely, Balance, Courage, Ego Strength, Emotional Control, Emotional Sensitivity,

and Neuroticism. For the purpose of this study both Neuroticism as the polar opposite of the

Emotional Stability cluster, and Emotional Stability will be explored.

De Raad (2000) notes the name of the factor Neuroticism has changed a number of times,

yet currently appears to be known as Neuroticism or Emotional Stability. The term Emotional

Stability is given preference in psycholexical approaches, especially when it is viewed as a

positive resource in an organisational context. The term Neuroticism is preferred in the tradition

45

of personality questionnaire construction and in contexts where neurotic behaviour is regarded as

problematic (de Raad, 2000).

According to the SAPI inventory, Neuroticism is defined as the emotional unsteadiness

or instability of person, the disposition of being cowardly, the quality of being dependent, and

lack of self efficacy, lack of emotional control and expression, and inability to handle

challenging life situations. Furthermore, neurotics have a propensity to be dissatisfied and

complain, and are prone to depressive moods and stress (Nel et al., 2012). According to the five

factor model, Neuroticism is a dimension of normal personality. Its presence signifies a tendency

to experience negative affect, namely, fear, sadness, embarrassment, worry, anger, guilt, and

disgust (Laher, 2008a; Rothmann & Coetzer, 2003; Visser & du Toit, 2004). The authors further

add that individuals who score high in Neuroticism are more inclined to experience psychiatric

problems (de Bruin, 2002; Laher, 2008a). In addition the individual is likely to experience

anxiety and feel insecure, as opposed to self-confident and composed (Emotional Stability). High

scores on Neuroticism make a person prone to experiencing irrational thought and ideas, being

impulsive, as well as poor stress management and coping. According to Nel et al. (2012),

Neuroticism encompasses the following facets: complaining, and content, depressive, neurotic,

and tense. Conversely, low Neuroticism or high Emotional Stability indicates that an individual

is usually calm, imperturbable, relaxed, as well as able to manage stress and its accompanying

emotions effectively (Hough, Eaton, Dunnette, Kamp & McCloy, 1990; Rothmann & Coetzer,

2003).

46

In line with the above, the NEO-PI-R defines Neuroticism as encompassing the following

aspects anxiety, anger-hostility, depression, self-consciousness, impulsiveness and vulnerability

(Laher, 2008b). Furthermore Brophy and Raubenheimer (1978) note that a neurotic’s inability to

function properly under normal conditions has been addressed by many writers. These authors

note that Neuroticism’s associated symptoms are uncomfortable by their very nature, creating

tension within the individual. Such an individual is characterised by being tense, and perhaps

even ill, and who seeks immediate relief from such unwanted tension. Furthermore, according to

Coleman (1972), neurotics are unable to function at capacity level.

Brophy and Raubenheimer (1978) acknowledge that a neurotic - in comparison to a non-

neurotic - is not able to compete on an equal footing under normal working conditions. Despite

the above-mentioned shortcomings of being a neurotic, Brophy and Raubenheimer (1978) advise

that such an individual be judged objectively on their work ability irrespective of such

shortcomings. In research conducted by Zhang and Akande (2002) Black students scored

significantly higher on Neuroticism compared to white students. However such results have been

attributed to differences in terms of educational level, socio-economic status and cultural

differences.

In terms of gender differences in the level of Neuroticism, Costa, Terracciano and

McCrae (2001) found that across 26 different cultures, women displayed higher levels of

Neuroticism than men in the majority of the cultures studied. Further research conducted in the

United States notes that the levels of Neuroticism between late adolescence and old age decline

for both men and women alike (McCrae, 2004).

47

With regard to internal consistency, the Cronbach alpha coefficient for Neuroticism on

the NEO-PI-R is said to be 0.92 (Rothmann & Coetzer, 2003). However, in their research,

Rothmann and Coetzer (2003) obtained a Cronbach alpha coefficient of 0.86. In research

conducted by Taylor (2000), a Cronbach alpha coefficient of 0.94 was ascertained. In addition,

Vogt and Laher (2009) attained a value of 0.95, whereas Ramsay et al (2008) obtained a

coefficient alpha of 0.91. Furthermore, in the study by Ramsay et al. (2005) the Nguni, Sotho

and Pedi groups obtained alpha coefficients of 0.90, 0.91 and 0.92 respectively. Chrystal (2012)

obtained an alpha coefficient for a total Neuroticism scale of 0.96. She further obtained good

Cronbach alpha coefficients across language groups with alpha values of α = 0.95 for the

Germanic group, α = 0.95 for the Nguni group and α = 0.97 for the Sotho group. Lastly,

Cronbach alpha coefficients for the five factor model have been acceptable. Most notably, in

research conducted by Hendriks et al. (2003) alpha coefficients for Neuroticism were 0.83 and

0.80 for a Dutch and American sample respectively. These values are acceptable when compared

to the cut-off point of 0.80 recommended by Nunnally and Bernstein (1994).

Against this background, this study aims to build towards an indigenous theory of

psychology, including personality theory, which will ultimately enable the accurate and fair

assessment of the South African population, further facilitating a better understanding of its

diverse, multicultural society. Currently no valid indigenous model of personality exists in South

Africa. However, the SAPI project, with its combined emic-etic approach proves thus far to be a

promising endeavour in the creation of an indigenous personality model and inventory. The

appropriate use of personality assessments in South Africa’s multicultural context depends on the

psychometric equivalence of such a measure across the respective cultures (Chrystal, 2012).

48

3.8 Ethical considerations

Ethical issues pertaining to the administration and interpretation of the SAPI inventory

include the anonymous collection of data. Furthermore, informed consent was obtained from all

the participants. This informed them of the nature of the study, and that confidentiality would be

guaranteed. Participation was voluntary and participants were free to withdraw their participation

at any stage of administration. Overall feedback was offered to participants who requested such

feedback and was in the form of a written report. No deception in terms of the purpose of the

study took place, thus no risk of harm was present, inferring no need for participant debriefing

after the measure was administered. Organisational and institutional permission was obtained

prior to test administration.

3.9 Conclusion

This chapter explored the emic and etic approaches to personality research, noting that a

combined approach is most useful in ascertaining context-specific as well as universal

personality traits. The current status of personality measurement in South Africa was discussed,

noting the need for an indigenously-developed, cross-cultural applicable personality inventory.

Individualism and collectivism as constructs influencing the manifestation of personality and

behaviour were investigated, followed by a description of bias and equivalence, and their

appearance in cross-cultural personality research. Subsequently the South African Personality

Inventory project was discussed, noting its purpose and current progress, with reference made to

the ES scale. This scale and other inventories measuring ES were explored and comparisons

drawn. Lastly the ethical considerations of the research project were discussed.

49

Chapter 4

Method

4.1 Introduction

This chapter will describe the methods used in analysing the Emotional Stability scale of

the SAPI. First the participants and data collection procedures are described, followed by a

description of the instrument and analytic techniques.

4.2 Participants and Procedures

A non-random, convenience sample of participants was recruited from several

organisations across industries in South Africa to complete paper and pencil copies of the SAPI.

The respective corporations and the individual participants were contacted, and consent was

obtained for their involvement. A total of 891 questionnaires were completed, providing a

sufficient sample for statistical analyses. The sample comprised of 50.11% of men (443) whereas

441 (49.89%) were women (see Table 4.1). The racial distribution was as follows: 34.17%

White; 47.02% Black; 7.80% Indian; and 11.01% Coloured (see Table 4.2). The split of the

various language groups measured was: 59.12% Germanic; 24.03% Nguni; and 16.86% Sotho

(see Table 4.3). Participants rated their proficiency in English as very poor (0.68%), poor (0.91

%), good (39.43 %) or very good (58.98 %) (see Table 4.4). With respect to highest educational

qualification, the majority of the participants (42.42%) had Grade 12; followed by 16.59% with a

Bachelor’s degree and 11.61% with an Honours degree (see Table 4.5).

50

Table 4.1

Demographic Composition of the Sample According to Gender

Gender Frequency Percentage

Male 443 50.11

Female 441 49.89

Total 884 100

Missing 7 0.79

Table 4.2

Demographic Composition of the Sample According to Racial Group

Racial Groups Frequency Percentage

White 298 34.17

Black 410 47.02

Indian 68 7.80

Coloured 96 11.01

Total 872 100

Missing 19 2.13

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Table 4.3

Demographic Composition of the Sample According to Language Group

Language Group Frequency Percentage

Germanic 470 59.12

Nguni 191 24.03

Sotho 134 16.86

Total 795 100

Missing 96 10.77

Table 4.4

Demographic Composition of the Sample According to English Reading Ability

English Reading Ability Frequency Percentage

Very poor 6 0.68

Poor 8 0.91

Good 347 39.43

Very good 519 58.98

Total 880 100

Missing 11 1.23

52

Table 4.5

Demographic Composition of the Sample According to Education Level

Education Level Frequency Percentage

Grade 9 37 4.38

Grade 12 358 42.42

Certificate 80 9.48

Diploma 81 9.60

Bachelor's 140 16.59

Honours 98 11.61

Masters 19 2.25

Doctorate 1 0.12

Other 30 3.55

Total 844 100

Missing 7 0.79

4.3. Instrument

Demographic questionnaire: The demographic questionnaire comprised items denoting the

respondents’ age, gender, race, home language, self-rated English reading ability, and highest

educational level.

53

The Emotional Stability scale reflects one of the nine clusters of the SAPI inventory. This

cluster consists of six sub-clusters, namely, Balance; Courage; Ego Strength; Emotional Control;

Emotional Sensitivity; and Neuroticism. This Emotional Stability scale consists of 33 items

selected from the pool of SAPI items. Participants respond to the items on a five-point Likert

scale, ranging from1 (Strongly Disagree) to 5 (Strongly Agree). Table 4.6 gives the 33 items of

the SAPI Emotional Stability scale. The item numbers reflect the serial position of the items in

the omnibus SAPI.

Table 4.6

ES Scale Items and Content

Items Content

i014 I am calm in most situations

i016 I control my emotions

i020 I calm down quickly

i023 I remain cheerful even when there are

problems

i036 I act in a mature manner

i043 I admit when I am wrong

i045 I get angry easily

i082 I can handle difficult situations

54

i084 I am afraid of people judging me

i095 I am afraid that bad things may happen

i102 I worry a lot

i105 I am afraid of some people

i108 I can deal with difficulties in my life

i120 I speak before I think

i125 I do things without thinking too much in

advance

i134 I do things that I later regret

i164 I get angry over minor issues

i177 I feel emotions deeply

i179 I cry easily

i212 I complain about everything

i215 I never get what I want

i225 I am pleased with what I have

i226 I accept things as they are

i244 I have lost interest in life

55

i248 I find it difficult to trust others

i253 I easily get nervous

i262 I want to be noticed

i267 I want people to listen to me

i272 I want to be respected

i280 I am difficult to please

i299 I depend on other people’s opinions

i317 I accept myself

i324 I respect myself

4.4 Statistical analyses

Analyses were conducted using the psych package (Revelle, 2012) within the statistical

programme R 2.14.1 (R Development Core Team, 2011). Descriptive statistics with regards to the means, SDs,

Cronbach alpha coefficients, skewness, and kurtosis were obtained. Factor analysis and differential item functioning analysis were used to

examine the psychometric properties and cross-cultural equivalence of the Emotional Stability scale.

4.4.1 Data screening

The data set was checked for unexpected responses, missing data, and outliers, prior to

being loaded in R. Descriptive statistics of the data were inspected, where the means, SDs,

skewness and kurtosis of the items were examined for unexpected values. Each of the means and

56

standard deviations appeared appropriate and each of the skewness and kurtosis coefficients fell

within the acceptable range.

4.4.2 Factor Analysis

An exploratory hierarchical unweighted least squares factor analysis was conducted on

the responses to the items for the collective group (i.e., the pooled data of the three language

groups) with the objective of investigating the underlying dimensionality of the Emotional (cf.

Gorsuch, 1983; McDonald, 1999; Wolff & Preising, 2005). The unweighted least squares

(minres) method is useful when the item distributions are irregular (Nunnally & Bernstein,

1994). The number of factors to extract was determined on the basis of the scree-plot, parallel

analysis (O’Connor, 2000; Watkins, 2000), interpretability of the factors, and the size of the

residuals (as reflected by the Root Mean Squared Residual, Root Mean Square Error of

Approximation, Tucker Lewis Index, and Tanaka Goodness of fit index). A second-order factor

analysis was executed on the correlations of the obliquely rotated (Direct Oblimin) first-order

factors, subsequent to which a Schmid-Leiman transformation was applied to attain the

hierarchical orthogonal solution (cf. Wolff & Preising, 2005).

4.4.3 The Schmid-Leiman transformation

The Schmid-Leiman transformation of a second-order factor analysis (SLS; Schmid &

Leiman, 1957) allows for the evaluation of the influence of a higher order factor on the first

order variables (i.e., the items) It further allows for a comparison of the relative impact of the

higher order factor on the one hand, and the primary factors on the other hand (Wolff & Preising,

2005). Stated differently, the SLS obtains the independent influence of the first and higher order

57

factors on a set of variables or items (Chernyshenko, Stark & Chan, 2001; Wolff & Preising,

2005).

This facilitates the interpretation of factors by clearly indicating each respective factor’s

unique influence on variables or items. By linking the higher order factor to the items, the

content of the higher order factor is easily identifiable, which aids in theory building and

research (Wolff & Preising, 2005).

The factors of the Emotional Stability scale were contrasted across the language group by

means of a coefficient of congruence (Tucker’s phi coefficient). Values above 0.90 were taken as

indicating factorial similarity (van de Vijver & Leung, 1997; van de Vijver & Poortinga, 1994).

4.4.4 Differential item functioning and differential test functioning analysis

Differential item functioning (DIF) analysis was used to evaluate the functioning of items

across language groups (Teresi, 2006). According to van Dam, Earleywine and Danoff-Burg

(2009) DIF occurs when individuals with an equal standing on a construct (as assessed by the

scale) have a different probability of selecting a particular response option on an individual item.

As such the scale scores should predict item invariance in terms of the participant’s response

option selected, as opposed to group membership. Consequently an item lacks invariance, or

displays DIF, when individuals with similar levels on a construct possess different probabilities

of selecting a given response option, as a result of their group (language, race, gender)

membership (van Dam et al., 2009).

58

Both anova and the Liu-Agresti common odds ratio (which is an expansion of the

Mantel-Hanzel technique) were used to analyse the DIF within the reduced scale (L-A LOR;

Liu-Agresti, 1996). Penfield (2007a) notes that L-A LOR is potentially a more robust measure of

DIF as it is better equipped to deal with extreme deviations in proportion of responses compared

to alternative measures of DIF. Van Dam et al. (2009) note that the L-A LOR depends on the

log odds ratio of one group electing a specific response option relative to the other group, which

is stratified by the global level of the construct. The respective L-A LOR value typically ranges

from -1 to 1, yet may increase beyond this range if the DIF is large. Negative values of L-A LOR

indicate bias against the reference group; while positive values indicate bias against the focal

group (van Dam et al., 2009).

Items were compared between language groups, where items with the highest DIF were

taken out of the analysis. Table 4.7 illustrates the criteria used to ascertain the magnitude of DIF

an item possessed: LOR > 0.64 Large DIF (ETS Class C); LOR > 0.43 but < 0.64 Moderate DIF

(ETS Class B); LOR < 0.43 Negligible DIF (ETS Class A) (Penfield & Algina, 2006; Zieky,

1993). Furthermore LOR Z values > 2.00 was an additional criterion in determining the

magnitude of an item’s DIF.

Table 4.7

Differential Item Functioning Categorisation Scheme of Zieky (1993)

DIF value L-A LOR Classification Interpretation

< .43 A Negligible

.43 - .64 B Slight to moderate

> .64 C Moderate to large

59

Items for the Emotional Stability scale were retained on the ensuing foundation: 1) items

that load robustly on factors in a psychologically significant way (> 0.30); 2) items that operate

equivalently across groups (Chrystal, 2012).

4.4.5 Differential test functioning

Differential test functioning (DTF) evaluates the impact of multiple DIF items on the test

level (van Dam et al., 2009). Stated differently, DTF ascertains the cumulative effect that item

DIF has across all the items on a scale. Penfield and Algina (2006) note that a measure or scale

exhibits biased functioning if more than 25% of the items comprising the scale displays moderate

to large DIF. The DTF value is represented by the symbol ν² with a cutoff of 0.14. Table 4.8

provides the criteria to ascertain and categorise the magnitude of DTF.

Table 4.8

Differential Test Functioning Categorisation Scheme of Penfield and Algina (2006)

DIF effect variance ν² % of items that possess LOR

Small < 0.07 less than approximately 10%

of the items have LOR ≥ 0.43

Medium 0.07 < ν² < 0.14

Large > 0.14 approximately 25% or more of

the items have LOR ≥ 0.43

Note. LOR = Liu Agresti log odds ratio

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4.4.6 Reliability

The reliability of the scale was computed by means of Cronbach’s coefficient alpha

(Cronbach, 1951). Cronbach alpha coefficients convey significant information concerning the

proportion of error variance subsumed in a scale (DeVellis, 1991; Netemeyer, Bearden &

Sharma 2003; Pett, Lackey & Sullivan 2003).

4.5 Conclusion

This chapter explored and described the statistical analyses utilised within this research

project. Furthermore the demographic characteristics of the participants were conveyed, in

addition to composition of the ES scale; the focus of the present study. The following chapter

will present the results of the analyses.

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Chapter 5

Results

5.1 Introduction

This chapter denoted the results of the exploratory factor analysis of the Emotional

Stability scale. Following the data screening and preliminary data analyses, the various criteria

for determining the number of factors to retain are reported. Thereafter the first and second order

hierarchical factor analyses are stated, followed by the Schmid-Leiman transformation.

The reliability of the total scale and its subscales is recounted followed by the congruence

analyses for all comparison language groups. Subsequently, the ES scale is reduced by deleting

19 items. The scale is then resubmitted to the above analyses. Finally, the results of differential

item functioning and differential test functioning analyses are reported.

5.2 Data screening

Prior to conducting the principal statistical analyses, the data were screened for

accurateness, omitted values, normality of distribution and outliers. Thereafter the feasibility of

the data was ascertained through inspection of minimum and maximum values, means, and

standard deviations. No unanticipated values were discovered across the scoring range (1 to 5);

and no cases were removed from the analyses. The item scores were adequately distributed with

all values for skewness < 2 and kurtosis < 4 respectively. Table 5.1 summarises the means, SDs,

skewness and kurtosis of the 33 items of the Emotional Stability scale.

62

Table 5.1

Descriptive Statistic of the Emotional Stability Scale

Mean SD Median Skew Kurtosis

i001 2.86 1.14 3 0.03 -0.89

i016 3.39 1.10 4 -0.44 -0.48

i043 2.58 1.21 2 0.35 -0.81

i047 2.56 1.20 2 0.43 -0.75

i048 2.91 1.12 3 0.14 -0.77

i055 2.01 1.00 2 1.03 0.83

i060 3.22 1.07 3 -0.02 -0.65

i065 4.60 0.63 5 -1.65 3.11

i069 3.84 1.03 4 -0.73 0.02

i075 2.95 1.18 3 -0.04 -0.93

i080 3.18 1.14 3 -0.15 -0.78

i087 2.63 1.05 3 0.27 -0.51

i093 2.35 0.96 2 0.60 0.16

i094 2.54 1.07 2 0.34 -0.71

i099 1.85 1.13 1 1.22 0.44

i105 3.86 0.94 4 -0.78 0.54

i115 3.60 0.99 4 -0.48 -0.17

i118 4.19 0.87 4 -1.12 1.24

i121 3.87 0.87 4 -0.73 0.61

i137 2.64 1.19 3 0.28 -0.86

i139 2.87 1.20 3 0.17 -0.92

i148 4.02 0.82 4 -0.95 1.51

i184 3.14 1.17 3 -0.03 -0.90

63

i193 4.21 0.81 4 -1.22 2.15

i200 3.94 0.85 4 -0.88 1.00

i203 3.37 0.93 3 -0.28 -0.21

i206 2.68 1.06 3 0.16 -0.67

i208 3.64 0.89 4 -0.72 0.54

i212 4.14 0.75 4 -1.08 2.42

i231 3.93 0.77 4 -0.77 1.25

i240 3.66 0.98 4 -0.67 0.13

i247 4.12 0.74 4 -0.88 1.68

i254 2.79 1.30 3 0.15 -1.14

i257 2.73 1.12 3 0.29 -0.74

5.3 Deciding the number of factors to retain

A factor analysis was performed on the ES scale using the minres method. The number of

factors to retain was decided on the basis of several criteria, namely the scree plot, parallel

analysis, Root Mean Squared Residual (RMSR), Root Mean Squared Error of Approximation

(RMSEA), Tucker Lewis Index (TLI), and the Tanaka Goodness of Fit Index (GFI). Figure 5.1

gives the scree and parallel analysis plots for the component model and the common factor

model, respectively.

Inspection of the respective scree plot yielded equivocal results which suggested that

between six to eight factors be extracted. Analysis of the parallel scree suggested that six factors

for the component model and eight factors for the factor analytic model be retained.

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Four indices of fit were used to ascertain the model’s fit, namely the Tanaka GFI; RMSR;

RMSEA; and the Tucker Lewis (1973) index (TLI). The former three indices include that of

absolute indexes whereas the latter is a form of incremental indexes of fit (Nasser & Takahashi,

2003). Tanaka (1993) noted that different indices emphasise different aspects of fit. Therefore,

obtaining various indicators of model fit is practical and desirable.

The RMSR is a model fit index which is the square root of the mean-squared differences

between the matrix elements in the observed matrix and model-implied, reproduced variance-

covariance matrix (Schumacker & Lomax, 2004). The RMSR relays the congruence between the

sample and reproduced correlation matrices. In terms of the RMSR, values between 0.05 and

0.08 are acceptable whilst values below 0.05 indicate excellent fit between the theory and the

data. Values above 0.08 are regarded as unacceptable and indicate poor fit between the theory

and the data. This is corroborated by Browne and Cudeck (1993) and Hu and Bentler (1995) who

note that the RMSR should be less than 0.08, and ideally less than 0.05 (Steiger, 1990). In this

study, the RMSR was 0.02 indicating an excellent fit between the factor model and the data.

The RMSEA represents the incongruity between the observed and reproduced correlation

matrices (Steiger & Lind, 1980). Smaller values of the RMSEA indicate a better fit between the

selected number of factors and the observed data (de Bruin, 2006). It is further congruent with

the goal of parsimony, in terms of explaining as much of the variance of the intercorrelations

matrix as possible with as few factors as feasible (Browne & Cudeck, 1993).

An RMSEA estimate equal to zero indicates a perfect fit between the factor model and

the observed data. Browne and Cudeck (1993) provide the following estimates recommended for

65

RMSEA: < 0.05 indicates a close fit; > 0.05 and < 0.08 indicates a satisfactory fit; and > 0.10

indicates a weak fit.

Tanaka GFI measures the approximate amount of variance and covariance within the data

set accounted for by the ES scale (Nasser & Takanashi, 2003). Tanaka GFI values that exceed

0.90 indicate a reasonable model fit (Hu & Bentler, 1995).

The TLI compares the lack of fit of a specific model to the lack of fit of a baseline model,

usually referred to as the null model (Nasser & Tanakashi, 2003). For a reasonable fit, the value

of the TLI should exceed 0.90 (Hu & Bentler, 1995).

Table 5.2 below summarises the RMSR, RMSEA, TLI and Tanaka GFI for five, six,

seven, and eight factor solutions, respectively.

Table 5.2

Criteria to Determine Factor Retention

Factors 5 6 7 8

RMSR 0.02 0.02 0.02 0.02

RMSEA 0.041 0.038 0.035 0.032

TLI 0.84 0.86 0.89 0.91

Tanaka GFI 0.95 0.96 0.97 0.98

66

Despite the relatively large TLI, it was thus decided with the aid of the actual ES scale,

the interpretability of the scale, analysis of the parallel and scree plot, residuals and fit indices

that five factors be retained as this appeared to be psychologically appropriate.

Figure 5.1. Scree and parallel analysis plots for a component model and a common factor model

5.4 Hierarchical factor analysis

An oblique (Direct Oblimin) five-factor solution was obtained and the correlations

between the factors were subjected to a second-order factor solution. The solution was

67

subsequently transformed to a hierarchical Schmid-Leiman solution with one general factor and

five primary factors.

Thereafter coefficient omega hierarchical (cf. McDonald, 1999) was obtained for all

respective language groups (Germanic, Nguni, and Sotho). The provided solutions were exposed

to a higher order factor analysis with the Schmid-Leiman transformation to ascertain if a higher

order factor was indeed present among the 33 items. Furthermore, these results would provide a

description on the factor congruence between the general factor and each respective language

group. Figure 5.2 below graphically depicts a hierarchical Schmid-Leiman solution with one

general factor and five primary factor.

Figure 5.2. Hierarchical five-factor solution for the ES scale. Negative factor loadings on the

primary factors are indicated by broken lines.

68

As can be seen from the table below (Table 5.3) only 14 of the 33 (42%) items loaded

robustly (> 0.30) on the general factor namely: i043, i047, i118, i105, i247, i231, i055, i121,

i240, i065, i206, i184, i048, and i212. Such loadings ranged from 0.48 to 0.32 respectively. This

suggests that divergent from expectations, the 33 items did not reveal a single higher order

factor.

Table 5.3

Hierarchical Schmid-Leiman Solution for the Emotional Stability Scale Items

g F1 F2 F3 F4 F5 h2 u2 p2

i043 -0.48 0.63 0.64 0.36 0.37

i047 -0.45 0.59 0.56 0.44 0.36

i105 0.44 0.42 0.31 0.49 0.51 0.4

i121 0.42 0.44 0.38 0.62 0.47

i247 0.42 0.37 0.36 0.64 0.48

i231 0.41 0.33 0.33 0.67 0.51

i240 0.41 0.38 0.33 0.67 0.5

i055 -0.41 0.27 0.73 0.61

i118 0.40 0.53 0.45 0.55 0.37

i206 -0.39 0.26 0.74 0.58

i065 0.38 0.49 0.39 0.61 0.37

i184 -0.36 0.45 0.37 0.63 0.34

i048 -0.33 0.39 0.3 0.7 0.35

i212 0.33 0.29 0.71 0.33

i139 0.47 0.33 0.67 0.25

i148 0.17 0.83 0.45

i099 0.32 0.26 0.74 0.26

i087 0.18 0.82 0.39

i200 0.13 0.87 0.51

69

i254 0.39 0.23 0.77 0.29

i094 0.13 0.87 0.48

i115 0.34 0.2 0.8 0.3

i080 0.40 0.24 0.76 0.23

i257 0.16 0.84 0.31

i075 0.46 0.29 0.71 0.16

i137 0.08 0.92 0.47

i093 0.35 0.18 0.82 0.22

i060 0.37 0.21 0.79 0.19

i203 0.11 0.89 0.19

i069 0.42 0.24 0.76 0.04

i193 0.33 0.21 0.79 0.04

i208 0.17 0.83 0.04

i001 0.33 0.13 0.87 0.03

i016 0.05 0.95 0.03

Note. Factor loadings < 0.30 are omitted to facilitate interpretation

The alpha coefficient of the whole scale was 0.82, with the omega hierarchical coefficient

being 0.56. Furthermore, the RMSR was 0.02 which is below the cutoff of 0.08, indicating a

satisfactory fit between the factor model and the data set.

Of the five factors that were extracted, eight items loaded saliently on the first factor, six

items loaded saliently on the second factor, two items loaded saliently on the third and fourth

factors, and five items loaded saliently on the fifth factor.

5.5 Coefficient of congruence for the whole Emotional Stability scale

70

To evaluate the similarity of the hierarchical five-factor solution across language groups,

Tucker’s phi was calculated, where the solution of each group was compared with the solution

obtained on the collective group. The congruence coefficients were computed for each of the five

factors across each of the respective language groups (Germanic, Nguni, and Sotho) in order to

establish construct equivalence. The coefficient ranges from 0 (no congruence) to 1 (perfect

congruence). To establish the stability of the ES scale across the three denoted groups, the higher

order ES factor of the collective group was contrasted with the higher order factor of each

language group. The results are reported below.

5.5.1 Group factors across language groups

To determine the construct equivalence across different language groups, it was proposed

that within each comparative group, the coefficient of congruence would be > 0.90 for all factors

(van de Vijver & Leung, 1997; van de Vijver & Poortinga, 1994). Separate analyses were

performed for each language group. In each group, five factors were retained. The factor

congruence matrices for the three language groups are displayed below in Tables 5.4 to 5.6.

Furthermore a Schmid-Leiman hierarchical factor solution for the different language groups is

graphically depicted in Figures 5.3 to 5.5.

71

Figure 5.3. Schmid-Leiman Hierarchical Factor Solution for Germanic group with one general

factor and five primary factors which shows that a higher order factor was indeed present within

this language group.

72

Table 5.4

Factor Congruence of the Collective Group and the Germanic Language Group

G F1 F2 F3 F4 F5

G 0.99 -0.32 0.71 0.6 -0.62 -0.17

F1 -0.4 0.94 -0.15 -0.08 0.15 0.29

F2 0.73 -0.01 0.96 0.24 -0.18 -0.08

F3 0.55 -0.04 0.24 0.91 -0.12 0

F4 -0.59 0.11 -0.15 -0.14 0.98 0

F5 -0.24 -0.09 0.04 -0.45 0.12 0.58

Note. Coefficients of congruence of corresponding factors are printed in bold face.

The coefficient of congruence between the factors of the collective group and those of the

Germanic language group displayed good agreement for factors 1 (Tucker’s phi = 0.94), 2

(Tucker’s phi = 0.96), 3 (Tucker’s phi = 0.91), and 4 (Tucker’s phi = 0.98). Tucker’s phi for the

general factor was equal to 0.99, which shows very strong agreement between the general factor

of the Germanic group and the collective group.

73

Figure 5.4. Schmid-Leiman Hierarchical Factor Solution for Nguni Group with one general

factor and five primary factors which illustrates that a higher order factor was indeed present

within this language group yet is not as robust as the Germanic language group.

74

Table 5.5

Factor Congruence of the Collective Group and the Nguni Language Group

g F1 F2 F3 F4 F5

G 0.96 0.30 -0.50 0.57 -0.57 0.57

F1 -0.36 -0.80 0.04 -0.20 0.00 -0.20

F2 0.68 0.04 -0.81 0.22 -0.60 0.11

F3 0.63 0.00 -0.05 0.67 -0.57 0.14

F4 -0.52 -0.10 0.09 -0.27 0.06 -0.90

F5 -0.33 -0.29 0.09 -0.30 0.07 -0.17

Note. Coefficients of congruence of corresponding factors are printed in bold face.

Table 5.5 summarises the coefficient of congruence between the collective group and the

Nguni language group which displayed good agreement only for group factor 5 with a Tucker’s

phi (factor pattern coefficient) of -0.90. Tucker’s phi for the general factor was equal to 0.96,

which again shows strong congruence between the general factor of the Nguni group and the

general factor of the collective group.

75

Figure 5.5. Schmid-Leiman Hierarchical Factor Solution for Sotho Group with one general

factor and five primary factors. This indicated that a higher order factor was indeed present

within this language group; however this group shows the weakest indication of the presence of a

higher order factor in comparison to the Germanic and Nguni language groups.

76

Table 5.6

Factor Congruence of the Collective Group and the Sotho Language Group

g F1 F2 F3 F4 F5

G 0.98 -0.51 0.76 -0.38 0.49 -0.41

F1 -0.37 0.49 -0.15 0.19 0.27 0.63

F2 0.71 0.01 0.89 -0.24 0.34 -0.08

F3 0.53 -0.18 0.32 -0.05 0.63 -0.33

F4 -0.64 0.73 -0.27 0.31 -0.28 0.08

F5 -0.22 -0.18 0.05 0.63 -0.25 0.41

Note. Coefficients of congruence of corresponding factors are printed in bold face.

Table 5.6 summarises the coefficient of congruence between the collective group and the

Sotho language group. It displayed no agreement between any of the five factors, signifying that

the facets did not measure the same construct. The Tucker’s phi for the general factor was equal

to 0.98, displaying good agreement between the general factor of the Sotho group and the

general factor of the collective group.

Upon examination of the outputs for both the collective group and each respective

language group, the scale was reduced to that of 14 items on which the factor process was

repeated. Items that loaded robustly (> 0.30) on the general factor were retained.

77

5.6 Reduced Emotional Stability scale

5.6.1 Deciding the number of factors to retain

Upon completion of the above analyses it was decided that the ES scale should be

reduced to those items that loaded saliently on the general factor (> 0.30). Thus the scale was

reduced to 14 items. This reduced ES scale was subject to a minres factor analysis, utlilising

several criteria to determine the number of factors to retain. The criteria were similar to those

utilised on the whole ES scale namely, parallel analysis and scree plot; RMRS; RMSEA; TLI;

and Tanaka GFI. Figure 5.6 depicts the parallel analysis scree plot. The remaining factor

retention criteria are discussed below.

Figure 5.6. Scree and parallel analysis plots for a component model and a common factor model.

The parallel analysis and scree plot suggested that the number of factors was equal to five, and

78

the number of components was equal to three. The scree plot suggested that two factors be

retained. The RMSR, the RMSEA, the TLI, and the Tanaka GFI were 0.03; 0.07; 0.86; 0.97

respectively.

Upon taking into consideration all the factor retention criteria, it was decided that an

oblique three-factor solution yielded the most useful results, appearing to be psychologically

appropriate.

5.6.2 Hierarchical factor analysis

An oblique (Direct Oblimin) three-factor solution was obtained and the correlations

between the factors were subjected to a second-order factor solution. The solution was

subsequently transformed to a hierarchical Schmid-Leiman solution with one general factor and

three primary factors. This solution is graphically portrayed in Figure 5.7.

79

Figure 5.7. Hierarchical three-factor solution for the ES reduced scale. Negative factor loadings

on the primary factors are indicated by broken lines.

As can be seen from the table below (Table 5.7), 12 of the 14 items (86%) loaded

saliently on the general factor. Such loadings ranged from 0.55 to 0.36 respectively. Items i184

and i048 fell slightly below the cutoff of 0.30. This suggests that - divergent from expectations -

the 14 items of the reduced scale did not reflect a single higher order factor.

80

Table 5.7

Hierarchical Schmid Leiman Solution for the Reduced Emotional Stability Scale Items

g F1 F2 F3 h2 u2 p2

i247 0.55 0.40 0.60 0.77

i231 0.52 0.34 0.66 0.79

i121 0.50 0.34 0.66 0.75

i105 0.50 0.31 0.69 0.80

i240 0.48 0.30 0.70 0.78

i212 0.42 0.23 0.77 0.75

i118 0.42 0.71 0.67 0.33 0.26

i055 -0.41 -0.24 0.76 0.68

i043 -0.40 0.64 -0.58 0.42 0.28

i065 0.39 0.39 0.31 0.69 0.49

i206 -0.39 -0.20 0.80 0.75

i047 -0.36 0.69 0.61 0.39 0.21

i184 0.37 0.24 0.76 0.33

i048 0.37 0.21 0.79 0.34

Note. Loadings < 0.30 are omitted.

The alpha coefficient of the reduced scale was 0.81, with the Omega Hierarchical being

0.62. This is an improvement from the whole ES scale with an Omega Hierarchical of 0.56.

81

The RMSR of the reduced scale was 0.03, which fell well below the cutoff of 0.08,

indicating a good fit between the factor model and the data. Of the three factors that were

extracted, none loaded saliently on the first factor, four loaded saliently on the second factor, and

two loaded saliently on the third factor.

5.6.3 Coefficient of congruence for reduced Emotional Stability scale

Separate hierarchical factor solutions were obtained for each language group. The factors

of each language group were then compared with the factors obtained in the combined group by

means of the coefficient of congruence (Tucker’s phi) in order to examine construct equivalence.

The factor congruence matrices for the three language groups are presented below in Tables 5.8

to 5.10 and Figures 5.8 to 5.10.

82

Figure 5.8. Schmid-Leiman Hierarchical Factor Solution for the Germanic group reduced scale

with one general factor and three primary factors. This indicated that a higher order factor was

indeed present within this language group.

83

Table 5.8

Factor Congruence of the Collective Group and the Germanic Language Group

g F1 F2 F3

G 0.99 0.88 -0.49 0.45

F1 0.88 0.98 -0.10 0.16

F2 -0.50 -0.21 0.98 -0.06

F3 0.36 0.12 -0.06 0.97

Note. Coefficients of congruence of corresponding factors are printed in bold face.

The coefficient of congruence between the Collective group and the Germanic group

displayed good agreement for all the factors with a Tucker’s phi obtained for factors 1, 2, and 3

of 0.98; 0.98; and 0.97 respectively. Tucker’s phi for the general factor was 0.99, which indicates

strong agreement between the general factor of the Germanic group and the collective group.

84

Figure 5.9. Schmid-Leiman Hierarchical Factor Solution for the Nguni group reduced scale with

one general factor and three primary factors. This indicated that a higher order factor was present

within this language group, yet is not as robust as the higher order factor for the Germanic

language group.

85

Table 5.9

Factor Congruence of the Collective Group and the Nguni Language Group

g F1 F2 F3

G 0.95 -0.42 0.73 0.63

F1 0.93 -0.10 0.79 0.63

F2 -0.28 0.78 -0.15 -0.03

F3 0.40 -0.09 0.17 0.44

Note. Coefficients of congruence of corresponding factors are printed in bold face.

The coefficient of congruence between the collective group and the Nguni language

group displayed no agreement between any of the three respective factors, signifying that the

facets did not measure the same construct. The Tucker’s phi for the general factor was 0.95,

again indicating good agreement between the general factor of the Nguni group and the

collective group.

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Figure 5.10. Schmid-Leiman Hierarchical Factor Solution for the Sotho group reduced scale

with one general factor and three primary factors. This indicated that a higher order factor was

indeed present within this language group. This is a vast improvement from the Schmid-Leiman

factor solution for the whole ES scale.

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Table 5.10

Factor Congruence of the Collective Group and the Sotho Language Group

g F1 F2 F3

G 0.96 -0.67 0.65 0.67

F1 0.73 -0.35 0.83 0.51

F2 -0.70 0.91 0.06 -0.24

F3 0.38 -0.09 0.09 0.60

Note. Coefficients of congruence of corresponding factors are printed in bold face.

The coefficient of congruence between the collective group and the Sotho language group

displayed good agreement only for factor 1 with a Tucker’s phi being 0.91. Tucker’s phi for the

general factor was equal to 0.96, which indicates good agreement between the general factor of

the Sotho group and the collective group.

5.7 Conditional analysis of variance of item responses across groups

Upon completion of the above analyses, the items of the reduced scale were subjected to

a conditional anova with the aim of identifying items that function differently across language

groups. The conditional anova approach is an extension of Cleary and Hilton’s (1968) use of

analysis of variance to identify bias in prediction. The conditional anova was used to ascertain

whether the items possessed bias in the form of uniform DIF, non-uniform DIF, both, or neither

(van de Vijver & Leung, 1997). The independent variables were trait level (where the total score

for the reduced scale was divided into eight score groups), and language group (Germanic,

Nguni, or Sotho), whereas the dependent variable was item scores.

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According to Kristjansson, Aylesworth, McDowell and Zumbo (2005) uniform DIF

occurs when an item is more difficult at all trait levels for one group compared to the other

group. Uniform DIF is indicated by a significant main effect for language groups. Conversely,

non-uniform DIF occurs when there is an interaction between ability level and group resulting in

the item being more difficult (Kristjansson et al., 2005). Non-uniform DIF is indicated by a

significant interaction effect of group membership and trait level. Table 5.11 below summarises

the results of the conditional anova DIF analysis across language groups.

Table 5.11

Conditional Analysis of Variance Probabilities of Uniform and Non-Uniform Differential

Item Functioning

Item Trait p Uniform p Non-uniform p

i043 < 0.001 0.77 < 0.001

i047 < 0.001 0.77 0.20

i048 < 0.001 0.01 0.71

i055 < 0.001 0.01 0.30

i065 < 0.001 < 0.001 0.58

i105 < 0.001 < 0.001 0.69

i118 < 0.001 < 0.001 0.79

i121 < 0.001 0.02 0.68

i184 < 0.001 0.19 0.64

i206 < 0.001 0.27 0.88

i212 < 0.001 < 0.001 0.01

i231 < 0.001 < 0.001 0.38

i240 < 0.001 0.48 0.97

i247 < 0.001 0.23 0.18

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Table 5.11 shows statistically significant uniform effects for eight of the 14 items of the

ES reduced scale. Furthermore two (i043 and i212) of the 14 items, displayed non-uniform DIF.

It therefore appeared that the conditional means of the language groups for the different score

levels systematically differ from zero. It is thus clear that the ES reduced scale exhibited uniform

DIF across the different language groups, while there appeared to be little evidence of non-

uniform DIF.

5.8 Differential Item Functioning and Differential Test Functioning Analysis

Upon completion of the anova approach a follow-up DIF and differential test function

(DTF) analysis was conducted in the DIFAS programme (Penfield, 2007b). The DIFAS

programme employs the Liu-Agresti log-odds ratio (L-A LOR), which is a non-parametric DIF

technique (Penfield, 2007b; Penfield & Algina, 2006).

The L-A LOR can also be converted to approximate z values which allow for statistical

significance tests of the DIF effects. The DIF identification and elimination process was

iterative. Sequentially, in each iteration, the item with the highest L-A LOR and LOR Z value >

2.00 was removed. The effect was then observed on the ν² statistic, which reflects the variance of

the DIF effects (Penfield & Algina, 2006). If the ν² remained above the cutoff of 0.14, an

additional round of item elimination was conducted, where the item with the highest L-A LOR

and LOR Z was removed. This process was repeated until the ν² fell below the cutoff of 0.14.

Three rounds of item elimination were needed for both the DIF analyses of the Germanic

and Nguni groups and the Germanic and Sotho groups respectively, prior to attaining this

criterion. Conversely no items met the criteria for elimination in the analysis of the Nguni and

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Sotho groups (see Table 5.16) Tables 5.12; 5.14; and 5.16 provide the results of these processes,

namely, the L-A LOR and LOR Z for the items that met the criteria for non-significant DIF.

Table 5.12

DIF Statistics: Polytomous Items for Germanic and Nguni Groups

Mantel L-A LOR LOR SE LOR Z

i043 0.58 -0.13 0.17 -0.74

i047 0.00 -0.01 0.17 -0.04

i048 9.81 -0.60 0.2 -3.06

i055 23.75 -0.93 0.19 -4.81

i065 4.65 0.41 0.19 2.14

i105 10.62 0.60 0.19 3.11

i118 10.21 0.55 0.18 3.08

i121 3.88 0.35 0.19 1.87

i184 0.02 -0.02 0.18 -0.12

i206 6.62 -0.58 0.23 -2.58

i212 2.71 -0.28 0.17 -1.66

i231 4.65 -0.36 0.17 -2.12

i240 2.52 -0.26 0.17 -1.54

i247 14.54 0.70 0.19 3.69

Note. Reference Value = 1, Focal Value = 2. Values > 0.64 are in bold face

Analysis of the Germanic and Nguni groups yielded a ν² value of 0.20 which indicated

that a DIF analysis be conducted to establish which items were problematic (see Table 5.13

below).

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Table 5.13

DTF Statistic: Polytomous Items for Germanic and Nguni Groups

DTF ν² SE Z

All items 0.20 0.09 2.26

Item i055 removed 0.15 0.07 2.06

Item i247 removed 0.13 0.07 1.94

Item i206 removed 0.10 0.06 1.76

Note. Reference Value = 1 Focal Value = 2

Subsequent to the analysis of the Germanic and the Nguni groups, the Germanic and the

Sotho groups were then analysed. The initial DTF yielded a ν² value of 0.26 indicating that a DIF

analysis needed to be undertaken to ascertain which items were problematic and that indeed

needed to be removed. Table 5.14 below provided the results of the DIF.

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Table 5.14

DIF Statistic: Polytomous Items for Germanic and Sotho Groups

Name Mantel L-A LOR LOR SE LOR Z

i043 0.557 0.143 0.193 0.741

i047 0.219 -0.095 0.194 -0.49

i048 12.562 -0.795 0.234 -3.397

i055 16.042 -0.888 0.222 -4.00

i065 1.324 0.248 0.219 1.132

i105 10.258 0.647 0.225 2.876

i118 1.692 0.268 0.209 1.282

i121 4.182 0.415 0.207 2.005

i184 2.105 0.29 0.203 1.429

i206 12.948 -1.024 0.292 -3.507

i212 2.593 -0.314 0.192 -1.635

i231 0.699 -0.159 0.194 -0.82

i240 6.59 -0.504 0.199 -2.533

i247 15.843 0.843 0.219 3.849

Note. Reference Value = 1, Focal Value = 3. Values > 0.64 are in bold face

As can be deduced from the above table, several L-A LOR scores were above the cutoff

of 0.64, in addition to several LOR Z scores being above 2.00. Three items were consequently

removed to ascertain a ν² value that adhered to the stipulated criteria (< 0.14).

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Table 5.15

DTF Statistic: Polytomous Items for Germanic and Sotho Groups

DTF ν² SE Z

All items 0.26 0.12 2.25

Item i206 removed 0.21 0.1 2.12

Item i055 removed 0.16 0.08 1.93

Item i247 removed 0.11 0.07 1.69

Note. Reference Value = 1 Focal Value = 3

The last analysis to be conducted was between the Nguni and the Sotho language groups.

Initially, a DTF was performed to ascertain whether the ν² was indeed below the cutoff of 0.14.

The analysis yielded a ν² of -0.01, indicating that the respective reduced scale is equivalent for

both groups. Table 5.17 provides a summary of the DTF result.

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Table 5.16

DIF Statistic: Polytomous Items for Nguni and Sotho Groups

Name Mantel L-A LOR LOR SE LOR Z

i043 1.144 0.246 0.228 1.079

i047 0.173 -0.098 0.234 -0.419

i048 0.901 -0.253 0.267 -0.948

i055 0.261 0.123 0.238 0.517

i065 0.314 -0.142 0.252 -0.563

i105 0.157 0.095 0.241 0.394

i118 1.088 -0.261 0.247 -1.057

i121 0.021 0.036 0.241 0.149

i184 1.251 0.257 0.231 1.113

i206 2.452 -0.497 0.322 -1.543

i212 0.013 0.026 0.223 0.117

i231 0.746 0.189 0.219 0.863

i240 1.477 -0.27 0.222 -1.216

i247 0.551 0.189 0.258 0.733

Note. Reference Value = 2, Focal Value = 3

Table 5.16 above provides a summary of the DIF analysis conducted on the Nguni and

Sotho language groups. As can be deduced, all L-A LOR scores were below the cutoff of 0.64, in

addition to all the LOR Z scores being below 2.00.

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Table 5.17

DTF Statistic: Polytomous Items for Nguni and Sotho Groups

DTF ν² SE Z

All items -0.01 0.02 -0.67

Note. Reference Value = 2, Focal Value = 3

5.9 Conclusion

This chapter depicts both graphically and in tabular format the results of the statistical

analyses conducted to ascertain the structural and item equivalence of the ES scale. The results

of the reduced ES scale appeared to indicate the presence of a higher order factor of ES across all

language groups. The empirical data presented in this chapter will be discussed in Chapter 6.

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Chapter 6

Discussion

6.1 Introduction

This chapter discusses the main findings conferred in the preceding chapter. The

hierarchical structure of the indigenous reduced ES scale is presented and compared with the

personality models reviewed in Chapter 2. The psychometric properties of the reduced scale are

deliberated in relation to previous research conducted in South Africa. Convergent and divergent

results are underscored and plausible explanations are provided. The findings are associated with

the pertinent postulates of the study, namely, to identify a common general ES factor across the

different language groups, in addition to determining the item equivalence of such a scale. The

chapter concludes with the limitations of the study and recommendations for future research.

The aim of the study was to ascertain the cross-cultural equivalence of the indigenous

Emotional Stability scale of the SAPI, which may be utilised to cross-culturally evaluate

Neuroticism in South Africa, further facilitating the theoretical comprehension of personality as

it manifests in this context. In doing so the equivalence of the respective scale was determined

across three language groups, namely, the Germanic; the Nguni; and the Sotho language groups.

This study contributes to the existing literature by developing an indigenous personality

scale that will be cross-culturally equivalent and valid. In the paragraphs that follow, each of the

study’s respective findings will be discussed. This will begin with the factor structure of the

reduced ES scale, and will then move on to the factor congruence of the reduced ES scale across

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the three language groups. Lastly the uniform and non-uniform DIF of the items in general and

the DIF of the reduced scale will be conferred.

In discussing such findings, the indigenous ES scale will be compared to that of previous

research findings of other personality models as reviewed in Chapter 2. This will encompass the

psychometric properties of such research embarked on in South Africa. Convergent and

divergent findings will be presented, and plausible explanations provided. The results presented

are further associated with the applicable hypotheses, and the performance of the indigenous ES

scale.

6.2 Emotional Stability in South Africa

The Emotional Stability scale of the SAPI is comprised of six group factors or subclusters

namely, Balance; Courage; Ego Strength; Emotional Control; Emotional Sensitivity; and

Neuroticism (Nel et al., 2012). In applying factor analysis to the reduced ES scale in this

respective project, three group factors or subclusters were identified, thus indicating the need to

explore the original six subclusters. This may in essence be due to one of the sub-clusters being

termed Neuroticism (the polar opposite of Emotional Stability), thus being negatively worded

and not robustly loading on six group factors.

6.3 Emotional Stability across language groups

Structural equivalence of the indigenous-reduced ES scale and its three subclusters was

ascertained using Tucker’s phi. Values of ≥ 0.90 indicate factorial similarity (van de Vijver &

Leung, 1997; van de Vijver & Poortinga, 1994). Tucker’s phi was computed for the higher order

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factor Emotional Stability by contrasting the total group with each language group. The results of

the present study exhibited factor equivalence of the indigenous Emotional Stability factor across

the three language groups assessed, signifying that the higher order dimension, Emotional

Stability, has the equivalent psychological meaning across all the groups. The Tucker’s phi

acquired for the factor Emotional Stability for each language group was: Germanic = 0.99;

Nguni =0.95; and Sotho = 0.96.

The results of the three group factors indicated coefficients of congruence at or above

0.90 for three group factors for the Germanic language group, no group factor for the Nguni

group, and one group factor for the Sotho group. However there was one group factor of the

Sotho group that possessed a congruence coefficient of 0.83, indicating that this factor may be

comprehended somewhat varyingly within this group.

This is in stark disparity to the results of van Eeden and Prinsloo (1997), who compared

African and Afrikaans/English speaking individuals utilising the SA version of the 16PF. Within

the study five group factors were found for the Afrikaans/English speaking sample yet just four

group factors were found for the African language speakers. A comparison between the two

language groups yielded a coefficient of congruence of 0.75, demonstrating construct non-

equivalence for the two groups.

Conversely, comparison studies of the Big Five are more favourable. Using the NEO-PI-

R, Heuchert, Parker, Stumpf and Myburgh (2000) compared a sample of Black and White

students and identified a five-factor structure with a congruence coefficient of 0.97 for

Neuroticism for the total sample when compared to the normative sample. Comparatively

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analogous were the results obtained for the White subgroup. The five-factor structure, however,

was less apparent for the Black subgroup. Despite this, target rotation of the Black subgroup

against the normative sample yielded a congruence coefficient for Neuroticism of 0.90. Target

rotation of the Black sample in contrast to the White sample signified a comparable personality

structure and a congruence coefficient of 0.88 for Neuroticism.

Heaven and Pretorius (1998) in their study compared the sample of Sotho and Afrikaans-

speaking students utilising John’s (1990) natural language descriptors. They were unable to

retrieve the five factor structure for the Sotho group. However, a distinct Neuroticism factor was

identified for both the Afrikaans and Sotho language groups.

The congruence coefficient of the BTI comparing Black and White groups indicated that

these groups contrast favourably with the findings of the indigenous Neuroticism scale. The

resulting congruence coefficient for the Neuroticism factor was 0.96. Supplementary

corroboration for measurement invariance across language groups (Nguni, Sotho and Pedi) on

the BTI was demonstrated by Ramsay et al. (2005). Furthermore, in validating an indigenous

Neuroticism scale, Chrystal (2012) obtained congruence coefficients of Germanic (pxy = 1.00),

Nguni (pxy = 1.00), and Sotho (pxy = .99).

Despite the limited congruence coefficients of the group factors, a robust general factor,

namely, Emotional Stability, was determined and found to retain its meaning across all language

groups. This is in line with the postulate of the study to explicitly define and find a general factor

that is equivalent across the different language groups.

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6.4 Hierarchical Schmid-Leiman factor solution

The Schmid-Leiman indicated that 12 of the 14 items loaded robustly on the general

factor. This is satisfactory as the general rule of thumb states that a scale should contain a

minimum of ten items. However, the number of items within this scale may not holistically

measure Emotional Stability and its sub-facets. Furthermore, no items loaded robustly on the

first group factor, and only two loaded saliently on the third group factor. In terms of scale

construction it is recommended that at least three items load saliently (> 0.30) on each group

factor. This did not affect the research outcomes, which was to identify a general factor for the

ES scale.

6.5 Reliability for the reduced Emotional Stability scale

The alpha coefficient for the reduced ES scale was 0.81, with an omega hierarchical of

0.62. The alpha coefficient indicates a good internal consistency in the reduced ES scale. This

study was the first to report an omega hierarchical value.

In comparison to previous research (cf. Ramsey et al., 2008; Rothmann & Coetzer, 2003;

Taylor, 2000), this value lies within the acceptable range. This differs from the findings of the

Meiring et al. (2005) study of the 15FQ+ which conveyed very low internal consistencies,

specifically for the Black language groups. The alpha coefficient of the Emotional Stability

scale was 0.64, which is below the referenced level of 0.70 (Nunnally & Bernstein, 1994), thus

indicating the scale’s incongruence for the South African context (Meiring et al., 2006).

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Abrahams and Mauer (1999) found similar results when they examined the cross-cultural

comparability of the 16PF (SA92) across four race groups. Their study yielded exceedingly low

reliability coefficients with alphas of below 0.50 for 13 of the 16 primary factors for the Black

group.

Comparable results were obtained by van Eeden and Mantsha (2007) who translated the

16PF into Venda (an African language) and administered the translated instrument to a Venda-

speaking sample. They recounted reliability coefficients of above 0.60 for only four of the 16

factors, despite removing items to improve reliability. The researchers further referred to issues

pertaining to the equivalent concepts in the target language, in addition to cultural variations in

the manifestation of the constructs, thereby, concluding the implausibility for validity and

equivalence studies.

Conversely the BTI (Taylor & de Bruin, 2006) exhibited comparable reliability

coefficients to that of the ES scale. Its Neuroticism factor yielded a reliability coefficient of 0.93.

An ensuing study by Ramsay et al. (2005) utlilising the BTI to measure Neuroticism across the

Nguni, Sotho and Pedi language groups uncovered reliability coefficients of 0.90; 0.91; and 0 .92

respectively. Furthermore research by Chrystal (2012) obtained a reliability coefficient of 0.96

for an indigenously developed Neuroticism scale. In addition she further obtained good

Cronbach alpha coefficients across language groups with alpha values of α = 0.95 for the

Germanic group, α = 0.95 for the Nguni group and α = 0.97 for the Sotho group.

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6.6 Differential item and test functioning

This was the first study to conduct DIF and DTF analyses on the ES scale. These

analyses began with a conditional anova. This analysis indicated the presence of uniform DIF in

the reduced ES scale, yet there appeared to be slight evidence of non-uniform DIF. Items i043 (‘I

admit when I am wrong’) and i212 (‘I complain about everything’) appeared to be most

problematic, possessing both uniform and non-uniform DIF.

In terms of comparison, no other research has been performed on this respective reduced

ES scale nor on any other ES or Neuroticism scale. As such these results are merely informative

of the non-equivalence of the scale for this respective study and as a result may not be

generalised to the larger population.

There appeared to be evidence of cultural specificities as manifested in DIF in addition to

the group factors of congruence that fell below the cutoff of 0.90. The DIF analysis of the

reduced ES scale indicated that three items be removed when the Germanic and Nguni group and

the Germanic and Sotho groups were compared. Of interest is that the same items were removed,

but in a different order (i055; i247; i206). Conversely no items were removed in the comparison

of the Nguni and Sotho groups, thus indicating item equivalence between the two groups.

The DTF of the reduced ES scale indicated significant test bias for the comparison of the

Germanic and the Nguni group and the Germanic and the Sotho group, which had ν² values of

0.20 and 0.26 respectively. This indicates that either comparison groups possessed large DIF

effect variance, and that approximately 25% or more of the items have LOR ≥ 0.43 (Penfield &

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Algina, 2006). With the removal of the aforementioned items, the DTF of the two comparison

groups subsided, thereby increasing the test’s equivalence.

The above DIF and DTF are the first to be conducted on this reduced ES scale and any

other ES or Neuroticism scale. In conjunction with the above conditional anova, these results

cannot be generalised to the larger population and merely provide guidance in the development

of this scale and its validation.

Previous studies have utilised imported measures of Neuroticism or ES within the South

African context. This study used an indigenous measure, which was developed with the aid of

the lexical method. Ashton and Lee (2005) note that the use of the lexical hypothesis and its

inherent methods should enable the testing of the generality of the derived factors through

conducting independent analyses in different languages; that is, the identified dimensions should

be replicable across languages. This was evident in this research project, in that the universality

of ES was determined across different language groups.

6.7 Limitations and suggestions for future research

The first limitation pertains to the naming of the ES scale and its sub-facets, namely, the

sub-facet of Neuroticism. As these two constructs are polar opposites and have been referred to

as such in previous research, it is thus recommended that the items of the ES scale be reviewed

and the subscale of Neuroticism be changed.

A further limitation of the study is that the resultant reduced ES scale contained

insufficient items to robustly measure ES and its various sub-facets. The general rule of thumb is

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that three to five items represent each of the subscales comprising the reduced scale. Accordingly

it is recommended that additional items be added to each subscale which will need further

analyses and validation. consequently

A third limitation is the low external validity of the results of the study. Due to the

sample not being demographically representative of the different language groups, the

generalisability of the results is limited. Furthermore, being the first study to conduct a

conditional anova and DIF and DTF analyses to ascertain item equivalence, the results remain

tentative, until further research may be conducted, either disputing or corroborating the findings.

Lastly, as Block (1995) indicates, the use of the lexical approach in the development of

personality inventories may prove to be fruitless, due to the multicultural and multilingual

society in which and for which it is created. Furthermore rapid and increasing globalisation,

urbanisation and acculturation, further impede the progress and process of such a venture,

namely, the development of an indigenous personality inventory (Visser & Viviers, 2010).

To develop a personality assessment for people within a specific context, people in that

respective context need to be understood. Using experts to develop traits and dimensions – not

the lexical approach, but rather their expertise – delivers a measure that is neither robust nor

representative of the given context (Ashton & Lee, 2005). As such the resultant instrument

would be confounded by experts’ areas of interest and knowledge, and would thus be neither

relevant nor progressive in depicting personality.

Despite these critics’ opinions, numerous studies involving the lexical method have been

fruitful. It is, however, imperative to remain cognisant of such limitations, using a holistic

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method and approach to counteract such limitations. Significant development of the ES scale is

needed; items need to be pooled and further analyses conducted. However, it may be noted that

this research project was successful in attaining its postulates, which therefore enables and

fosters the progress towards the development of an indigenous, robust, and equivalent ES scale.

6.8 Conclusion

The inherent goal of the SAPI project is to develop and validate an indigenous

personality inventory that is cross-culturally equivalent and applicable to South Africa’s

multicultural and multilingual society. The present study represents a progression towards this

goal, through signifying the cross-cultural applicability of Emotional Stability across three

language groups. This further solidifies the notion of the universality of Emotional Stability

(versus Neuroticism) as an intrinsic construct of personality, in addition to fulfilling the

stipulated requirements of the Employment Equity Act 55 of 1998 (section 8).

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