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Previous research on the fakeability of the Implicit Association Test (IAT) yielded inconsistent results. The present study simultaneously analyses several relevant factors: faking direction, type of instructions, and practice. Furthermore, it takes baseline individual differences into account. After a baseline assessment in a self-esteem IAT without faking instructions (t0), participants in the faking conditions then (t1) faked high or low scores without being provided with recommended strategies on how to do so (i.e., individual strategies). At t2 and t3, they were asked to fake the IAT after having received information on recommended faking strategies. At t4, faking direction was reversed. Without the recommended strategies, faking high scores was not possible, but faking low scores was. With the recommended strategies, participants needed additional practice to fake high scores. When faking directions were reversed, participants were successful without additional practice, suggesting a transfer in faking skills. In most of the faking attempts, faking success was moderated by individual differences in baseline implicit self-esteem. This suggests that the complex interplay of factors influencing faking success should be taken into account when considering the issue of fakeability of the IAT.

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: IAT v , which has already been shown to be significantly related to the construct-related variance of the IAT effect, and IAT a and IAT t0 , both of which have been assumed to provide an indication of faking. But research on the impacts of faking on IAT v , IAT a , and IAT t0 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, IAT v 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, IAT a 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). IAT t0 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.

Although faking on the Implicit Association Test (IAT) is a relevant problem, it has not yet been considered for the traditional IAT effect (D measure). Research has suggested that diffusionmodel-based IAT effects may be useful as IAT v is related to the construct-related variance and IAT a and IAT t0 have both been assumed to provide indications of faking. Recent research used fast-dm to reanalyze nonfaked and faked IAT data under various faking conditions (faking low vs. faking high scores in a naïve vs. informed manner). The results showed that faking affected IAT v . However, there was an impact on IAT a when people knew how to fake and had to fake low scores. Thus, diffusion model analyses deliver additional information, but they are also very complex to perform. The diffusion tool EZ is easy to handle and very powerful, but researchers do not yet know whether IAT v , IAT a , and IAT t0 deliver similar information about the components in IAT results when they are obtained with EZ. Thus, we used EZ to reanalyze the data set described above. The results from fast-dm and EZ were comparable, but EZ had somewhat higher statistical power. IAT v was impacted by faking, thus replicating the finding that diffusion model analyses cannot yet be used to completely separate construct-and faking-specific variance from each other. However, replicating and extending the findings that were obtained with fast-dm, informed faking had an impact on IAT a and IAT t0 , which might both serve as indicators of faking. Thus, our results indicate that EZ as well as fast-dm is a powerful tool that can help researchers to interpret IAT results.

Performance on implicit measures reflects construct-specific and nonconstruct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: Change in performance could reflect changes in the constructs of interest or changes in other mental processes. We reanalyzed data from six studies ( N = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race implicit association test (IAT) performance. Diffusion models decompose overall IAT performance ( D-scores) into construct-specific (ease of decision-making) and nonconstruct-specific processes (speed–accuracy trade-offs, non-decision-related processes like motor execution). Interventions that effectively reduced D-scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed–accuracy trade-offs between compatible and incompatible trials. Non-decision-related processes were affected by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.