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

An implicit association test (IAT) measures differential association of 2 target concepts with an attribute when instructions oblige highly associated categories to share a response key, and performance is faster than when less associated categories share a key.

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.