Abstract

6.1464) is often used to predict people's behaviors.However, it has shown poor predictive ability potentially because of its typical scoring method (the D score), which is affected by the across-trial variability in the IAT data and might provide biased estimates of the construct.Linear Mixed-Effects Models (LMMs) can address this issue while providing a Rasch-like parametrization of accuracy and time responses.In this study, the predictive abilities of D scores and LMM estimates were compared.The LMMs estimates showed better predictive ability than the D score, and allowed for in-depth analyses at the stimulus level that helped in reducing the acrosstrial variability.Implications of the results and limitations of the study are discussed.

Keywords

Implicit-association testPsychologyImplicit attitudeAssociation (psychology)Test (biology)Implicit personality theorySocial psychologyCognitionCognitive psychologySocial cognitionPersonality

MeSH Terms

AsianAttitudeEthnicityFemaleHumansJapanKoreaLogistic ModelsMalePsychometricsReaction TimeReproducibility of ResultsSelf ConceptStereotypingWashingtonWhite PeopleWord Association Tests

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Publication Info

Year
1998
Type
article
Volume
74
Issue
6
Pages
1464-1480
Citations
8881
Access
Closed

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Cite This

Anthony G. Greenwald, Debbie E. McGhee, Jordan L. K. Schwartz (1998). Measuring individual differences in implicit cognition: The implicit association test.. Journal of Personality and Social Psychology , 74 (6) , 1464-1480. https://doi.org/10.1037/0022-3514.74.6.1464

Identifiers

DOI
10.1037/0022-3514.74.6.1464
PMID
9654756

Data Quality

Data completeness: 86%