The Effect of Familiarity with the Response Category Labels on Item Response to Likert Scales

Weijters, Bert; Geuens, Maggie; Baumgartner, Hans

doi:10.1086/670394

Cited by 47 publications

(44 citation statements)

References 34 publications

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“…However, scale usage differences need not always occur at the respondent level, and under certain circumstances it may be preferable to correct for differential scale usage at the group level. For instance, prior research has shown that translations of response category labels can result in labels whose meaning varies across different languages, and that as a consequence endorsement rates may differ simply because nonequivalent labels were used in different languages (Skevington & Tucker, 1999;Szabo, et al, 1997;Weijters, et al, 2013). In particular, response category labels used in different languages may vary in intensity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, in a comparison of response category labels in the U.S. and Germany, Smith, et al (2005) found subtle intensity differences between apparently equivalent labels such as 'definitely agree' and its German translation, 'stimme bestimmt zu' (i.e., 'definitely' is a stronger term in English than 'bestimmt' is in German). Furthermore, Weijters et al (2013) recently demonstrated that a category label may be more idiomatic and thus more familiar in one language than another.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, 'tout à fait d'accord' in French is more familiar than 'strongly agree' in English and this leads to greater endorsement of the endpoint categories in French than in English, regardless of content (Weijters, et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although Greenleaf proposed these items to measure extreme responding, the heterogeneous content of the items makes them well-suited for quantifying differences in response patterns in general (Weijters, et al, 2013). We will use this scale in our empirical work and henceforth refer to the scale as the Response Pattern Scale (RPS).…”

Section: Estimating Group-specific Response Frequency Distributions Bmentioning

confidence: 99%

“…However, in certain situations scale usage heterogeneity may occur primarily at the group level, in which case it is more appropriate to model differential scale usage at the group level. For example, when Likert-type rating scales anchored by labels such as 'strongly (dis)agree' or 'completely (dis)agree' are used in different languages, the meaning of the response category labels may subtly but systematically vary across languages, which can lead to differences in scale usage at the group level (Skevington & Tucker, 1999;Smith, Mohler, Harkness, & Onodera, 2005;Szabo, Orley, & Saxena, 1997;Weijters, Geuens, & Baumgartner, 2013). Similarly, data collection modes or experimental manipulations may affect the perceived meaning of the category labels and thus induce scale usage heterogeneity (Jordan, Marcus, & Reeder, 1980;Weijters, Schillewaert, & Geuens, 2008).…”

Section: Introductionmentioning

confidence: 99%

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The calibrated sigma method: An efficient remedy for between-group differences in response category use on Likert scales

Weijters

Baumgartner

Geuens

2016

International Journal of Research in Marketing

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The authors propose a procedure, labeled the calibrated sigma method, which is designed to correct for between-group differences in endorsement likelihood of response categories that are unrelated to the content of the items. The method is especially useful in cross-cultural research where group differences may reflect variation in scale usage rather than substantive differences. However, the procedure is also relevant in other situations, for example, when different data collection modes or different experimental manipulations affect respondents' perception of the meaning of the scale labels. The calibrated sigma method uses information derived from heterogeneous control items (calibration items) to reweight the responses to substantive items in a group-specific way. The advantages of the calibrated sigma method are that it avoids the arbitrariness in the assignment of particular numerical values to response categories; that it is compatible with the linear model, which is used by most marketing researchers; and that it does not require the use of complex nonlinear models involving the estimation of many additional measurement model parameters. The authors validate the calibrated sigma method on a simulated cross-linguistic data set pertaining to 12 different languages; an empirical data set collected from respondents of the same nationality but from two different language groups; and an experimental data set consisting of responses to two different response scale formats. The findings demonstrate that the proposed procedure controls for artefactual scale use differences across groups but does not eliminate substantive differences. It is particularly efficient for marketing research agencies, panel providers and other marketing researchers who analyze surveys involving multiple language groups, different scale formats, multiple modes of data collection, or different manipulations affecting the meaning of the response category labels.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Estimating Group-specific Response Frequency Distributions Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The calibrated sigma method: An efficient remedy for between-group differences in response category use on Likert scales

Weijters

Baumgartner

Geuens

2016

International Journal of Research in Marketing

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References

2019

Measurement in Marketing

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When Cheap Isn't the Same as Not Expensive: Generic Price Terms and Their Negations

2018

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Generic price terms such as cheap, not expensive, not cheap, and expensive are used frequently in marketing, but little is known about how consumers interpret them. In typical consumer contexts, in which lower prices are preferred, we find that not cheap means essentially the same as expensive (i.e., not cheap = expensive). In contrast, not expensive does not mean the same as cheap, in that cheap is associated with lower prices (i.e., cheap < not expensive). As an implication, in a consumer survey we find different response distributions for rating scales that are anchored with endpoint labels of not expensive versus expensive as compared to cheap versus expensive or cheap versus not cheap (the latter two are equivalent). Furthermore, although cheap is associated with lower prices than not expensive, this does not necessarily translate into higher preferences for options that are described as cheap (vs. not expensive), because cheap may also evoke negative quality perceptions. Finally, we demonstrate an interesting reversal in the way price terms are interpreted: when higher prices are preferred, cheap is equal to not expensive (i.e., cheap = not expensive), whereas not cheap is associated with lower prices than expensive (i.e., not cheap < expensive).

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The Effect of Familiarity with the Response Category Labels on Item Response to Likert Scales

Cited by 47 publications

References 34 publications

The calibrated sigma method: An efficient remedy for between-group differences in response category use on Likert scales

The calibrated sigma method: An efficient remedy for between-group differences in response category use on Likert scales

References

When Cheap Isn't the Same as Not Expensive: Generic Price Terms and Their Negations

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