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