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 diffusion-model-based IAT effects may be useful as IATv is related to the construct-related variance and IATa and IATt0 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 naıve 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 whether IATv, IATa, and IATt0 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. IATv was im- pacted 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 IATa and IATt0, 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.