How to analyze (faked) Implicit Association Test data by applying diffusion model analyses with the fast-dm software: A companion to Röhner & Ewers (2016)

The Implicit Association Test (IAT) is a popular and frequently used measure in researchon implicit associations. However, an important drawback of the traditional computation of IATresults with the so-called D measure is that the D measure may veri1ably include more than justindications of the implicit associations that should be measured. It can also be contaminated by fakingand other sources of variance. The D measure does not differentiate between different sourcesof variance. With the help of diffusion model analyses, IAT results can be analyzed and interpretedin a more detailed manner because three separable IAT effects (i.e., IATv, IATa, and IATt0 ) can becomputed from the parameters from diffusion model analyses. These effects have been assumed toseparate faking- and construct-speci1c variance from each other. Thus, a possible advantage of usingdiffusion model analyses instead of the traditional IAT effect is that less contaminated and moreinterpretable IAT effects are produced (i.e., IATv, which captures the construct-related variance;IATa and IATt0 , which capture the faking-speci1c variance). This paper was written to demonstratehow to use the software fast-dm to compute these three newly developed IAT effects and todescribe how to interpret them.