Assessing the Performance of Strategies for Handling Rapid Guessing Responses in Item Response Theory Equating

Abstract

This study assesses the performance of strategies for handling rapid guessing responses (RGs) within the context of item response theory observed-score equating. Four distinct approaches were evaluated: (1) ignoring RGs, (2) penalizing RGs as incorrect responses, (3) implementing list-wise deletion (LWD), and (4) treating RGs as missing data followed by imputation using logistic regression-based methodologies. These strategies were examined across a diverse array of testing scenarios. Results indicate that the performance of each strategy varied depending on the specific manipulated factors. Both ignoring and penalizing RGs were found to introduce substantial distortions in equating accuracy. LWD generally exhibited the lowest bias among the strategies evaluated but showed higher standard errors. Data imputation methods, particularly those employing lasso logistic regression and bootstrap techniques, demonstrated superior performance in minimizing equating errors compared to other approaches.