Abstract
Research trying to uncover the true nature of faking is currently dominated by two competing modeling approaches. One approach views faking as the manifestation of distinct and qualitatively different response patterns. Typically, mixed Rasch models are used within this approach. The alternative approach views faking as a continuous and quantitative variable resulting from the interaction between test taker personality and situational demands. Modeling techniques for this approach range from regression analyses to structural equation modeling. So far, there has been no study in which both modeling approaches have been applied within one data set. More importantly, so far there has been no methodological model in which both views of faking could have been modeled simultaneously. Within the present article such a modeling approach is introduced and applied to a data set of N = 497 applicants. By combining factor mixture modeling with a latent change score model, it was possible to test both views of faking within the same model. Findings support the view of faking mainly as a continuous and quantitative variable. Theoretical implications are discussed.
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