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Lost in Translation?
The Effect of Language on
Response Distributions in Likert Data
Bert Weijters
Maggie 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)
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
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)
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
Method: Design and sample
Country
Language Dutch
Netherlands
Belgium
1046
644
French
371
France
Germany
1000
993
Italian
1046
1015
Italy
Total
1690
German
Total
Switzerland
1000
993
303
1674
606
1599
50
939
989
959
939
5952
Effect of language on response distributions in Likert data
Hierarchical clustering of regions by response
category proportions (Ward’s method)
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
Study 2: Design
version
MOTHER
TONGUE
Total
NL_a
NL_b
FR_a
FR_b
Dutch
115
61
62
128
366
French
109
224
57
118
51
113
112
240
329
695
Total
NL_a (A)
NL_ b (B)
FR_a (C)
FR_b (D)
5
4
Volledig eens
Enigszins eens
Sterk eens
Eerder eens
Tout à fait d'accord
Un peu d'accord
Fortement 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
Effect of language on response distributions in Likert data
Study 2: Design (cont’d)
 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);
Effect of language on response distributions in Likert data
Statistical analysis
Score Statistics For Type 3 GEE Analysis
Source
Questionnaire
Mother_tongue
Scale_Category
Questionnaire*Mother_tongue
Questionnaire*Scale_Category
Scale_Category*Mother_tongue
3-way interaction
DF
ChiSquare
Pr > ChiSq
3
1
3
3
9
3
9
24.11
0.12
422.09
3.35
73.32
4.92
10.70
<.0001
0.7297
<.0001
0.3402
<.0001
0.1777
0.2969
Effect of language on response distributions in Likert data
Study 2: Results
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
Two alternative hypotheses to explain
the effect of response category labels
Intensity hypothesis:
 H1: Endpoint labels with higher intensity are
less frequently endorsed.
Fluency hypothesis:
 H2: Endpoint labels with higher fluency are
more frequently endorsed.
Effect of language on response distributions in Likert data
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;
Extreme endpoint label
Shifting boundary
Narrow category
Low frequency
1
2
3
4
5
6
7
Overt Likert response
Latent construct
Effect of language on response distributions in Likert data
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;
Effect of language on response distributions in Likert data
Main experiment: Method
□ We randomly assigned Dutch speaking students (N = 100) to two
alternative versions of a brief online questionnaire (10 heterogeneous 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
Effect of language on response distributions in Likert data
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 (χ21=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);
Effect of language on response distributions in Likert data
Study 4: Method
Language
France
USA
Canada
UK
Total
French
227
0
203
0
430
English
0
185
196
187
568
227
382
399
187
998
Total
Version
1
2
3
4
5
6
English
French
Strongly agree
Completely agree
Extremely agree
Definitely agree
Fully agree
Very much agree
Fortement d'accord
Complètement d'accord
Extrêmement d'accord
Définitivement d'accord
Entièrement d'accord
Tout à fait d'accord
Effect of language on response distributions in Likert data
Multilevel results
Estimate
S.E.
Est./S.E.
P-Value
Within Level
ERS
ON
FEMALE
AGE
EDU_HI
0.057
-0.001
-0.048
0.047
0.003
0.085
1.196
-0.279
-0.560
0.232
0.781
0.575
Between Level
ERS
ON
FLUENCY
INTENSITY
LANG_FR
C_US
C_FR
C_UK
0.165
-0.133
0.061
0.119
0.007
0.025
0.064
0.131
0.087
0.102
0.076
0.120
2.594
-1.014
0.703
1.166
0.091
0.212
0.009
0.311
0.482
0.244
0.927
0.832
1.002
0.184
5.444
0.000
Intercept
ERS
Effect of language on response distributions in Likert data
Discussion: summary of findings
Nationality
Cross-regional
nonequivalence
Other
language
aspects
Language
Label intensity
Questionnaire
response
category labels
Label currency
Study 1: Cross-regional European survey
Response distributions are more homogeneous
for regions sharing the same language than for
regions sharing the same nationality.
Effect of language on response distributions in Likert data
Discussion: summary of findings
Nationality
Cross-regional
nonequivalence
Other
language
aspects
Language
Label intensity
Questionnaire
response
category labels
Label currency
Study 2: Experiment with bilinguals
Response distributions vary as a function of
category labels, even within the same language
and regardless of respondents’ mother tongue
Effect of language on response distributions in Likert data
Discussion: summary of findings
Nationality
Cross-regional
nonequivalence
Other
language
aspects
Language
Label intensity
Questionnaire
response
category labels
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 experiment
This finding holds in a multilingual cross-continental setting, irrespective of language
and nationality
Effect of language on response distributions in Likert data
Implications
 Response style research


Need to extend the scope to questionnaire
characteristics
Need to cross-validate/replicate earlier crossnational comparisons
 Cross-cultural survey research


Reconsider regional segmentations
Validate measures cross-linguistically and crossnationally
Effect of language on response distributions in Likert data
Implications for
multilingual 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.
Completely agree
Tout à fait d’accord
fluency
1.24
1.22
ERS%
18.8%
19.2%