This study investigates the predictive abilities of the Implicit Association Test (IAT) and compares them with those of Linear Mixed-Effects Models (LMMs). The IAT is a widely used measure to assess implicit socio-psychological constructs, but it has shown poor predictive ability for behavioral outcomes. The study proposes a Rasch analysis based on LMMs to address the across-trial variability in IAT data and improve predictive accuracy. The LMMs estimates were found to have better predictive ability than the traditional D score, and they allowed for in-depth analyses at the stimulus level, reducing across-trial variability. The results suggest that the proposed modeling framework can enhance the predictive power of the IAT while providing valuable insights into respondent performance and stimulus functioning. The study also highlights the importance of selecting informative and representative stimuli to reduce across-trial variability and improve the validity and efficiency of IATs.This study investigates the predictive abilities of the Implicit Association Test (IAT) and compares them with those of Linear Mixed-Effects Models (LMMs). The IAT is a widely used measure to assess implicit socio-psychological constructs, but it has shown poor predictive ability for behavioral outcomes. The study proposes a Rasch analysis based on LMMs to address the across-trial variability in IAT data and improve predictive accuracy. The LMMs estimates were found to have better predictive ability than the traditional D score, and they allowed for in-depth analyses at the stimulus level, reducing across-trial variability. The results suggest that the proposed modeling framework can enhance the predictive power of the IAT while providing valuable insights into respondent performance and stimulus functioning. The study also highlights the importance of selecting informative and representative stimuli to reduce across-trial variability and improve the validity and efficiency of IATs.