Implications of the Implicit Association Test D-Transformation for Psychological Assessment

摘要

Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used to assess a wide range of psychological states. The D-score algorithm for coding the Implicit Association Test (IAT) requires the presence of random noise in order to obtain variability. Without consequential degrees of random noise, all individuals receive extreme scores. We present results from an algebraic proof, a computer simulation, and an online survey of implicit racial attitudes to show how trial error can bias IAT assessments. We argue as a result that the D-score algorithm should not be used for formal assessment purposes, and we offer an alternative to this approach based on multiple regression. Our critique focuses primarily on the IAT designed to measure unconscious racial attitudes, but it applies to any IAT developed to provide psychological assessments within clinical, organizational, and developmental branches of psychologyand in any other field where the IAT might be used.