Röhner, J., & Thoss, P. J. (2019). A tutorial on how to compute traditional IAT effects with R. The Quantitative Methods for Psychology, 15(2), 134–147. https://doi.org/10.20982/tqmp.15.2.p134

The Implicit Association Test (IAT) is the most frequently used and the most popularmeasure for assessing implicit associations across a large variety of psychological constructs. Altogether,10 algorithms have been suggested by the founders of the IAT to compute what can be calledthe traditional IAT effects (i.e., the six D measures: D1, D2, D3, D4, D5, D6, and the four conventionalmeasures [C measures]: C1, C2, C3, C4). Researchers can decide which IAT effect they want to use,whereby the use of D measures is recommended on the basis of their properties. In this tutorial,we explain the background of the 10 traditional IAT effects and their mathematical details. We alsopresent R code as well as example data so that readers can easily compute all of the traditional IATeffects. Last but not least, we present example outputs to illustrate what the results might look like.