Röhner, J., & Thoss, P. J. (2018). EZ: An easy way to conduct a more fine-grained analysis of faked and nonfaked Implicit Association Test (IAT) data. The Quantitative Methods for Psychology, 14(1), 17–37. https://doi.org/10.20982/tqmp.14.1.p017

Although faking on the Implicit Association Test (IAT) is a relevant problem, it has not yetbeen considered for the traditional IAT effect (D measure). Research has suggested that diffusionmodel-based IAT effects may be useful as IATv is related to the construct-related variance and IATaand IATt0 have both been assumed to provide indications of faking. Recent research used fast-dmto reanalyze nonfaked and faked IAT data under various faking conditions (faking low vs. fakinghigh scores in a naïve vs. informed manner). The results showed that faking affected IATv. However,there was an impact on IATa 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 whetherIATv, IATa, and IATt0 deliver similar information about the components in IAT results when theyare obtained with EZ. Thus, we used EZ to reanalyze the data set described above. The results fromfast-dm and EZ were comparable, but EZ had somewhat higher statistical power. IATv was impactedby faking, thus replicating the 1nding that diffusion model analyses cannot yet be used tocompletely separate construct- and faking-specific variance from each other. However, replicatingand extending the 1ndings that were obtained with fast-dm, informed faking had an impact onIATa and IATt0 , which might both serve as indicators of faking. Thus, our results indicate that EZas well as fast-dm is a powerful tool that can help researchers to interpret IAT results.