Lost in Translation? The Effect of Language on Response Distributions in Likert Data

Lost in Translation? The Effect of Language on

Response Distributions in Likert Data

Bert WeijtersMaggie Geuens

Hans Baumgartner

Effect of language on response distributions in Likert data

The non-equivalence problem in cross-national research

Surveys are popular in cross-national marketing and consumer research

However, one common concern is that survey responses may not be equivalent across countries:□ the same response (e.g., ‘4’ on a five point-agree/ disagree

scale) may have a different meaning for different respondents (e.g., in different countries);

□ sources of non-equivalence: Item-specific (different meanings attached to a particular

item) General (i.e., over multiple tems)


Research objective General non-equivalence (i.e., bias not specific to a

particular item) may be due to□ Nationality or national culture□ Language

Our focus is on language and we will show that □ language differences can be a more important contributor to

scale usage differences than differences in nationality;□ at least for bilingual respondents, differences in mother

tongue do not matter;□ the scale labels used affect response behavior;□ the fluency (rather than the intensity) of the scale labels

seems to be the driver of differences in response behavior;


Method: Measuring response

distributions A major challenge is to measure bias in response

distributions that is not item-specific and independent of substantive content;

To do this, we need to observe patterns of responses across heterogeneous items (i.e., items that do not share common content but have the same response format):

Deliberately designed scales consisting of heterogeneous items (Greenleaf 1992)

Random samples of items from scale inventories (Weijters, Geuens & Schillewaert 2010)


Study 1 Does nationality or language lead to greater

similarity in responses to heterogeneous Likert items?

“Natural” experiment using native speakers of different languages in Europe who share or do not share the same nationality;


Method: Design and sample

Country

Netherlands Belgium France Germany Switzerland Italy Total

Language Dutch 1046 644 1690

French 371 1000 303 1674

German 993 606 1599

Italian 50 939 989

Total 1046 1015 1000 993 959 939 5952

Effect of language on response distributions in Likert dataHierarchical clustering of regions by

response category proportions (Ward’s method)


Study 2 Are differences in response distributions due to language

mainly related to respondents’ mother tongue (i.e., an individual characteristic) or the language of the questionnaire (i.e., a stimulus characteristic)?

In particular, does the use of different category labels within each language affect the response distributions?

□ Response category labels are a potential systematic source of differences in response distributions since they are constant across items but variable across languages;

□ Even within the same language, response distributions may differ if different response category labels are used;


versionTotalNL_a NL_b FR_a FR_b

MOTHERTONGUE

Dutch 115 61 62 128 366

French 109 57 51 112 329Total 224 118 113 240 695

NL_a (A) NL_ b (B) FR_a (C) FR_b (D)5 Volledig eens Sterk eens Tout à fait d'accord Fortement d’accord4 Enigszins eens Eerder eens Un peu d'accord Plutôt d’accord

3 Noch eens, noch oneens Neutraal Ni d'accord, ni pas d'accord

Neutre

2 Enigszins oneens Eerder oneens Un peu en désaccord Plutôt pas d’accord

1 Volledig oneens Sterk oneens Tout à fait en désaccord Fortement pas d’accord

Study 2: Design


Dependent variable:□ 16-item Greenleaf (1992) scale;□ 16 heterogeneous Likert items sampled from as many

unrelated marketing scales;□ the two sets of measures achieved convergent validity

and were combined; language profile (language proficiency and use of

Dutch/French);

Study 2: Design (cont’d)


Statistical analysis Score Statistics For Type 3 GEE Analysis

Chi-Source DF Square Pr > ChiSq

Questionnaire 3 24.11 <.0001Mother_tongue 1 0.12 0.7297Scale_Category 3 422.09 <.0001Questionnaire*Mother_tongue 3 3.35 0.3402Questionnaire*Scale_Category 9 73.32 <.0001Scale_Category*Mother_tongue 3 4.92 0.17773-way interaction 9 10.70 0.2969

Effect of language on response distributions in Likert dataStudy 2: Results


More specific tests Interaction of questionnaire version and scale category

shows that the response patterns differ by language and/or label;

In both Dutch and French, using different label sets changed the response distributions;

Depending on which labels are used in Dutch and French, response distributions may or may not vary across languages;


Discussion Study 2 response distributions do not seem to differ as a function

of a respondent’s mother tongue; the language of the questionnaire and the labels used for

the scale categories can have a substantial influence on how readily certain positions on the rating scale are endorsed:□ even within the same language, supposedly similar labels

strongly affected responses to items that were presumably free of common content;

□ in a multi-language context, where category labels do differ across languages but are common across items within the same language, the labels attached to different scale positions can be a potent source of response bias;


Two alternative hypotheses to explain the effect of response

category labelsIntensity hypothesis:

H1: Endpoint labels with higher intensity are less frequently endorsed.

Fluency hypothesis:

H2: Endpoint labels with higher fluency are more frequently endorsed.


H1: Intensity hypothesis Item Response Theory:

□ respondents map their standing on the latent variable onto the response category that covers their position on the latent variable (Samejima 1969; Maydeu-Olivares 2005);

□ the wider the response category, the more likely respondents are to endorse it; more intense endpoint labels move the category’s lower or upper boundary away

from the midpoint, resulting in lower response frequencies;

1 2 3 4 5 6 7 Overt Likert response

Latent construct

Extreme endpoint label Shifting boundaryNarrow categoryLow frequency


H2: Fluency hypothesis Research on processing fluency shows that the meta-cognitive experience

of ease of processing affects judgment and decision making:□ perceptions of the truth value of statements (e.g., Unkelbach 2007)□ liking for objects and events (e.g., Reber, Schwarz, and Winkielman

2004)□ choice deferral or choices of compromise options (e.g., Novemski et al.

2007); Repeated statements are more likely to be rated as true (Unkelbach 2007)

and repetition increases liking, as suggested by the mere exposure effect (e.g., Bornstein 1989), in part because repetition makes stimuli more familiar and contributes to greater processing fluency;

Therefore, if scale labels are more commonly used in everyday language and are thus easier to process, this should increase the likelihood that the corresponding response option on the rating scale is selected;


Main experiment: Method

□ We randomly assigned Dutch speaking students (N = 100) to two alternative versions of a brief online questionnaire (10 hetero-geneous Likert items and pairwise comparisons);

□ Two endpoint versions: ‘sterk (on)eens’ (‘strongly (dis)agree’): low intensity, low fluency

‘volledig (on)eens’ (‘fully (dis)agree’): high intensity, high fluency


Main experiment: Findings A generalized linear model analysis showed that the

number of extreme positive responses was significantly lower in the ‘sterk eens’ (low intensity and fluency) condition than in the ‘volledig eens’ (high intensity and fluency) condition: means of 3.63 vs. 4.44 (χ2

1=3.998, p = .046);

This result is consistent with H2: labels that are more fluent lead to higher response category frequencies (in this case despite their higher intensity);


Study 4: Method

France USA Canada UK Total

Language French 227 0 203 0 430

English 0 185 196 187 568

Total 227 382 399 187 998

Version English French

1 Strongly agree Fortement d'accord2 Completely agree Complètement d'accord3 Extremely agree Extrêmement d'accord4 Definitely agree Définitivement d'accord5 Fully agree Entièrement d'accord6 Very much agree Tout à fait d'accord


Multilevel results Estimate S.E. Est./S.E. P-ValueWithin Level ERS ON FEMALE 0.057 0.047 1.196 0.232 AGE -0.001 0.003 -0.279 0.781 EDU_HI -0.048 0.085 -0.560 0.575

Between Level ERS ON FLUENCY 0.165 0.064 2.594 0.009 INTENSITY -0.133 0.131 -1.014 0.311 LANG_FR 0.061 0.087 0.703 0.482 C_US 0.119 0.102 1.166 0.244 C_FR 0.007 0.076 0.091 0.927 C_UK 0.025 0.120 0.212 0.832 Intercept ERS 1.002 0.184 5.444 0.000


Discussion: summary of findings

Cross-regional non-equivalence

Nationality

Language

Other language aspects

Questionnaire response category labels

Label intensity

Label currency

Study 1: Cross-regional European surveyResponse distributions are more homogeneous for regions sharing the same language than for regions sharing the same nationality.



Nationality

Language



Label intensity

Label currency

Study 2: Experiment with bilingualsResponse distributions vary as a function of category labels, even within the same language and regardless of respondents’ mother tongue




Nationality

Language



Label intensity

Label fluency

Study 3: Label experiment (one sample)Highly fluent labels lead to higher endorsement rates of response categories, irrespective of label intensity (and keeping language constant)

Study 4: Cross-continental label experimentThis finding holds in a multilingual cross-continental setting, irrespective of language and nationality



Implications Response style research

Need to extend the scope to questionnaire characteristics

Need to cross-validate/replicate earlier cross-national comparisons

Cross-cultural survey research Reconsider regional segmentations Validate measures cross-linguistically and cross-

nationally


Implications formultilingual survey research

□ Translations usually imply a trade-off between the attempt to be literal and the attempt to be idiomatic;

□ Optimize equivalence: use response category labels that are equally fluent in different languages (rather than literal translations or words with equal intensity);

e.g., ‘Strongly agree’ is most commonly used in scales, but may not have valid equivalents in some other languages. ‘Completely agree’ seems to be a viable alternative.

fluency ERS%Completely agree 1.24 18.8%Tout à fait d’accord 1.22 19.2%

Documents

Lost in Translation? The Effect of Language on Response Distributions in Likert Data