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Understanding and using the Implicit Association Test: I. An improved scoring algorithm (2003)
| Venue: | Journal of Personality and Social Psychology |
| Citations: | 632 - 94 self |
BibTeX
@ARTICLE{Greenwald03understandingand,
author = {Anthony G. Greenwald and T. Andrew Poehlman and Eric Luis Uhlmann and Mahzarin R. Banaji and Anthony G. Greenwald},
title = {Understanding and using the Implicit Association Test: I. An improved scoring algorithm},
journal = {Journal of Personality and Social Psychology},
year = {2003},
pages = {197--216}
}
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Abstract
behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures. Parallel explicit (i.e., self-report) measures, available in 156 of these samples (13,068 subjects), also predicted effectively (average r=.361), but with much greater variability of effect size. Predictive validity of self-report was impaired for socially sensitive topics, for which impression management may distort self-report responses. For 32 samples with criterion measures involving Black–White interracial behavior, predictive validity of IAT measures significantly exceeded that of self-report measures. Both IAT and self-report measures displayed incremental validity, with each measure predicting criterion variance beyond that predicted by the other. The more highly IAT and self-report measures were intercorrelated, the greater was the predictive validity of each.
Keyphrases
implicit association test predictive validity improved scoring algorithm self-report measure sensitive topic research report criterion variance iat measure parallel explicit black white interracial behavior impression management physiological measure criterion measure self-report response independent sample behavior relation greenwald incremental validity effect size
