# Comparing the ability of the IAT and of the SC-IAT to account for behavioral outcomes: a re-analysis using linear mixed-effects models

## TL;DR

This study compares two implicit association tests (IAT and SC-IAT) using advanced statistical models to better understand their ability to predict behavior.

## Contribution

The study introduces a new modeling framework that accounts for data structure and design in comparing implicit measures.

## Key findings

- The IAT shows higher predictive validity compared to the SC-IAT.
- A specific SC-IAT variant contributes uniquely to predictions.
- Traditional scoring methods may obscure certain SC-IAT effects.

## Abstract

Implicit measures are widely used to indirectly assess psychological constructs and predict behavior. Nonetheless, comparisons of their predictive validity often suffer from methodological limitations, including administration inconsistencies, scoring differences, and unaccounted sources of variability related to data structure and experimental design.

To address these issues, the present study re-analyzes an existing dataset comparing the Implicit Association Test (IAT) and its single-category variant (SC-IAT) using a modeling framework that integrates a Rasch-like parameterization of accuracies and response times while accounting for the fully crossed data structure and the within-subject design.

Results partially align with the original findings and further corroborate the higher predictive validity of the IAT, while revealing the specific contribution of one SC-IAT that was likely obscured in traditional scoring approaches.