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

Michelangelo Vianello, Yoav Bar-Anan

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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. 396). 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.

Original languageEnglish
Pages (from-to)415-421
Number of pages7
JournalPerspectives on Psychological Science
Volume16
Issue number2
DOIs
StatePublished - 1 Mar 2021
Externally publishedYes

Keywords

  • IAT
  • Implicit Association Test
  • multitrait multimethod design
  • structural equation models
  • validity

ASJC Scopus subject areas

  • Psychology (all)

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