Measuring individual differences in implicit cognition: The implicit association test.

1998/01/01 by Anthony G. Greenwald, Debbie E. McGhee, Jordan L. K. Schwartz · 18 citations
Neuroscience · Psychology · #Motor Control and Adaptation #Implicit-association test #Psychology #Implicit attitude #Association (psychology) #Test (biology) #Implicit personality theory #Social psychology #Cognition #Cognitive psychology #Social cognition #Personality

paper · doi:10.1037/0022-3514.74.6.1464

openalex publication_date 1998/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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.

Citations

• Fitting Linear Mixed-Effects Models Using lme4
• Understanding and using the Implicit Association Test: I. An improved scoring algorithm.

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