Can the Implicit Association Test Measure Automatic Judgment? The Validation Continues

In this commentary, we welcome Schimmack's reanalysis of Bar-Anan and Vianello's multitrait multimethod (MTMM) data set, and we highlight some limitations of both the original and the secondary analyses. We note that when testing the fit of a confirmatory model to a data set, theoretical justifications for the choices of the measures to include in the model and how to construct the model improve the informational value of the results. We show that making different, theory-driven specification choices leads to different results and conclusions than those reported by Schimmack (this issue, p. diamond diamond diamond ). Therefore, Schimmack's reanalyses of our data are insufficient to cast doubt on the Implicit Association Test (IAT) as a measure of automatic judgment. We note other reasons why the validation of the IAT is still incomplete but conclude that, currently, the IAT is the best available candidate for measuring automatic judgment at the person level.

Automated summary

In this commentary, the reanalysis of the MTMM dataset is welcomed but limitations in both original and secondary analyses are highlighted. Theoretical justifications for choices in modeling improve result interpretation, and different specification choices can lead to different conclusions. Despite other reasons for incomplete validation of the IAT, it is currently considered the best measure of automatic judgment at the person level.