Literature DB >> 27109866

A closer look at the discrimination outcomes in the IAT literature.

Rickard Carlsson1, Jens Agerström1.   

Abstract

To what extent the IAT (Implicit Association Test, Greenwald et al., 1998) predicts racial and ethnic discrimination is a heavily debated issue. The latest meta-analysis by Oswald et al. (2013) suggests a very weak association. In the present meta-analysis, we switched the focus from the predictor to the criterion, by taking a closer look at the discrimination outcomes. We discovered that many of these outcomes were not actually operationalizations of discrimination, but rather of other related, but distinct, concepts, such as brain activity and voting preferences. When we meta-analyzed the main effects of discrimination among the remaining discrimination outcomes, the overall effect was close to zero and highly inconsistent across studies. Taken together, it is doubtful whether the amalgamation of these outcomes is relevant criteria for assessing the IAT's predictive validity of discrimination. Accordingly, there is also little evidence that the IAT can meaningfully predict discrimination, and we thus strongly caution against any practical applications of the IAT that rest on this assumption. However, provided that the application is thoroughly informed by the current state of the literature, we believe the IAT can still be a useful tool for researchers, educators, managers, and students who are interested in attitudes, prejudices, stereotypes, and discrimination.
© 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Keywords:  Implicit association test; ethnic discrimination; meta-analysis; racial discrimination

Mesh:

Year:  2016        PMID: 27109866     DOI: 10.1111/sjop.12288

Source DB:  PubMed          Journal:  Scand J Psychol        ISSN: 0036-5564


  8 in total

1.  Applied Racial/Ethnic Healthcare Disparities Research Using Implicit Measures.

Authors:  Nao Hagiwara; John F Dovidio; Jeff Stone; Louis A Penner
Journal:  Soc Cogn       Date:  2020-12-01

2.  A meta-analysis of procedures to change implicit measures.

Authors:  Patrick S Forscher; Calvin K Lai; Jordan R Axt; Charles R Ebersole; Michelle Herman; Patricia G Devine; Brian A Nosek
Journal:  J Pers Soc Psychol       Date:  2019-06-13

3.  Is the performance at the implicit association test sensitive to feedback presentation? A Rasch-based analysis.

Authors:  Ottavia M Epifania; Egidio Robusto; Pasquale Anselmi
Journal:  Psychol Res       Date:  2022-07-08

4.  Interview with an avatar: Comparing online and virtual reality perspective taking for gender bias in STEM hiring decisions.

Authors:  Cassandra L Crone; Rachel W Kallen
Journal:  PLoS One       Date:  2022-06-07       Impact factor: 3.752

5.  Increasing self-other similarity modulates ethnic bias in sensorimotor resonance to others' pain.

Authors:  Ville Johannes Harjunen; Petja Sjö; Imtiaj Ahmed; Aino Saarinen; Harry Farmer; Mikko Salminen; Simo Järvelä; Antti Ruonala; Giulio Jacucci; Niklas Ravaja
Journal:  Soc Cogn Affect Neurosci       Date:  2022-07-02       Impact factor: 4.235

6.  What Bias Management Can Learn From Change Management? Utilizing Change Framework to Review and Explore Bias Strategies.

Authors:  Mai Nguyen-Phuong-Mai
Journal:  Front Psychol       Date:  2021-12-15

Review 7.  Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions.

Authors:  Franziska Meissner; Laura Anne Grigutsch; Nicolas Koranyi; Florian Müller; Klaus Rothermund
Journal:  Front Psychol       Date:  2019-11-08

8.  Race, Gender, and the U.S. Presidency: A Comparison of Implicit and Explicit Biases in the Electorate.

Authors:  Gemma Anne Calvert; Geoffrey Evans; Abhishek Pathak
Journal:  Behav Sci (Basel)       Date:  2022-01-17
  8 in total

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