Supplementary materials for the manuscript "Filling the gap between implicit associations and behavior: A Linear Mixed-Effects Rasch Analysis of the Implicit Association Test"

The measure obtained from the Implicit Association Test (IAT; Greenwald et al., 1998) 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 across-trial variability. Implications of the results and limitations of the study are discussed. Supplementary materials for: Epifania, O. M., Anselmi, P., & Robusto, E. (2022). Filling the Gap Between Implicit Associations and Behavior: A Linear Mixed-Effects Rasch Analysis of the Implicit Association Test. Methodology, 18(3), 185-202. https://doi.org/10.5964/meth.7155 supplementary-Filling-the-Gap: File containing descriptive statistics and full models for the choice prediction; RcodeFillingTheGap: commented R script for reproducing the results and/or analyse new data unknown unknown