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

• The calibrated sigma method efficiently corrects for scale usage heterogeneity.
• The method reweights responses to substantive items in a group-specific way.
• Controls for between-group scale use differences without eliminating true differences.
• Is compatible with the linear model and complements CFA

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.