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Cross-cultural comparison of attitudes using rating scales may be seriously biased by response styles. This paper deals with statistical methods for detection of and correction for extreme response style (ERS), which is one of the well-documented response styles. After providing an overview of available statistical methods for dealing with ERS, we argue that the latent class factor analysis (LCFA) approach proposed by Moors (2003) has several advantages compared to other methods. Moors' method involves defining a latent variable model which, in addition to the substantive factors of interest, contains an ERS factor. In LCFA the observed ratings can be treated as nominal responses, which is necessary for modeling ERS. We find strong evidence for the presence of ERS and, moreover, find that the groups differ not only in their attitudes but also in ERS. These findings underscore the importance of controlling for ERS when examining attitudes in cross-cultural research.13 14 MORREN ET AL.

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Prior research has shown that extreme response style can seriously bias responses to survey questions and that this response style may differ across culturally diverse groups. Consequently, cross-cultural differences in extreme responding may yield incomparable responses when not controlled for. To examine how extreme responding affects the cross-cultural comparability of survey responses, we propose and apply a multiple-group latent class approach where groups are compared on basis of the factor loadings, intercepts, and factor means in a Latent Class Factor Model. In this approach a latent factor measuring the response style is explicitly included as an explanation for group differences found in the data. Findings from two empirical applications that examine the cross-cultural comparability of measurements show that group differences in responding import inequivalence in measurements among groups. Controlling for the response style yields more equivalent measurements. This finding emphasizes the importance of correcting for response style in cross-cultural research.

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