@article{Epifania_Anselmi_Robusto_2022, title={Filling the Gap Between Implicit Associations and Behavior: A Linear Mixed-Effects Rasch Analysis of the Implicit Association Test}, volume={18}, url={https://meth.psychopen.eu/index.php/meth/article/view/7155}, DOI={10.5964/meth.7155}, abstractNote={<p>The measure obtained from the Implicit Association Test (IAT; Greenwald et al., 1998. DOI: 10.1037/0022-3514.74.6.1464) 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 across-trial variability. Implications of the results and limitations of the study are discussed.</p&gt;}, number={3}, journal={Methodology}, author={Epifania, Ottavia M. and Anselmi, Pasquale and Robusto, Egidio}, year={2022}, month={Sep.}, pages={185-202} }