Recent research has indicated that diffusion model analyses allow the user to decompose the traditional IAT effect (D measure) into three newly developed IAT effects: IATv, which has already been shown to be significantly related to the construct-related variance of the IAT effect, and IATa and IATt0, both of which have been assumed to provide an indication of faking. But research on the impacts of faking on IATv, IATa, and IATt0 is still warranted. By reanalyzing a data set containing both faked and unfaked IAT effects, we investigated whether diffusion model analyses could be used to separate construct-related variance from faking-related variance on the IAT. Our results revealed that this separation is not yet possible. As had already been shown for the traditional IAT effect, IATv was affected by faking. Interestingly, it was affected by faking only under more difficult faking conditions (i.e., when participants were asked to fake without being given recommended strategies for how to do so, and when they were requested to fake high scores). By contrast, IATa was affected by faking only in the comparably easy faking condition (i.e., when participants had been informed about possible faking strategies and were asked to fake low scores). IATt0 was not affected by faking at all. Our results show that although diffusion model analyses cannot yet provide a clear separation between construct- and faking-related variance, they allow us to peer into the black box of the faking process itself, and thus provide a useful tool for analyzing and interpreting IAT scores.
Adult Behavioral Research