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The Implicit Association Test (IAT) is a popular and frequently used measure in research on implicit associations. However, an important drawback of the traditional computation of IAT results with the so-called $D$ measure is that the $D$ measure may verifiably include more than just indications of the implicit associations that should be measured. It can also be contaminated by faking and other sources of variance. The $D$ measure does not differentiate between different sources of variance. With the help of diffusion model analyses, IAT results can be analyzed and interpreted in a more detailed manner because three separable IAT effects (i.e., $IAT_{v}$, $IAT_{a}$, and $IAT_{t_0}$) can be computed from the parameters from diffusion model analyses. These effects have been assumed to separate faking- and construct-specific variance from each other. Thus, a possible advantage of using diffusion model analyses instead of the traditional IAT effect is that less contaminated and more interpretable IAT effects are produced (i.e., $IAT_{v}$, which captures the construct-related variance; $IAT_{a}$ and $IAT_{t_0}$, which capture the faking-specific variance). This paper was written to demonstrate how to use the software fast-dm to compute these three newly developed IAT effects and to describe how to interpret them.

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