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Understanding and Using the Brief Implicit Association Test: Recommended Scoring Procedures

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  • Brian A Nosek
  • Yoav Bar-Anan
  • N Sriram
  • Jordan Axt
  • Anthony G Greenwald

Abstract

A brief version of the Implicit Association Test (BIAT) has been introduced. The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. The data transformation algorithms D outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques. We conclude with recommended analytic practices for standard use of the BIAT.

Suggested Citation

  • Brian A Nosek & Yoav Bar-Anan & N Sriram & Jordan Axt & Anthony G Greenwald, 2014. "Understanding and Using the Brief Implicit Association Test: Recommended Scoring Procedures," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-31, December.
    • Handle: RePEc:plo:pone00:0110938
    DOI: 10.1371/journal.pone.0110938
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    Cited by:

    1. Hendrik Slabbinck & Arjen van Witteloostuijn & Julie Hermans & Johanna Vanderstraeten & Marcus Dejardin & Jacqueline Brassey & Dendi Ramdani, 2018. "The added value of implicit motives for management research Development and first validation of a Brief Implicit Association Test (BIAT) for the measurement of implicit motives," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-29, June.
    2. Andrés Sánchez-Prada & Carmen Delgado-Alvarez & Esperanza Bosch-Fiol & Virginia Ferreiro-Basurto & Victoria A. Ferrer-Perez, 2020. "Psychosocial Implications of Supportive Attitudes towards Intimate Partner Violence against Women throughout the Lifecycle," IJERPH, MDPI, vol. 17(17), pages 1-20, August.
    3. Joan Martinez, 2022. "The Long-Term Effects of Teachers' Gender Stereotypes," Papers 2212.08220, arXiv.org, revised Jul 2023.
    4. Choi, Syngjoo & Hahn, Kyu Sup & Kim, Byung-Yeon & Lee, Eungik & Lee, Jungmin & Lee, Sokbae, 2024. "North Korean refugees’ implicit bias against South Korea predicts market earnings," Journal of Development Economics, Elsevier, vol. 169(C).

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