Entropy-Based Measures for Person Fit in Item ResponseTheory

摘要

This article introduces three new variants of entropy to detect person misfit (Ei, EMi, and EMRi), and provides preliminary evidence that these measures are worthy of further investigation. Previously, entropy has been used as a measure of approximate data–model fit to quantify how well individuals are classified into latent classes, and to quantify the quality of classification and separation between groups in logistic regression models. In the current study, entropy is explored through conceptual examples and Monte Carlo simulation comparing entropy with established measures of person fit in item response theory (IRT) such as lz, lz*, U, and W. Simulation results indicated that EMi and EMRi were successfully able to detect aberrant response patterns when comparing contaminated and uncontaminated subgroups of persons. In addition, EMi and EMRi performed similarly in showing separation between the contaminated and uncontaminated subgroups. However, EMRi may beadvantageous over other measures when subtests include a small number of items.EMi and EMRi arerecommended for use as approximate person-fit measures for IRT models. Thesemeasures of approximate person fit may be useful in making relative judgmentsabout potential persons whose response patterns do not fit the theoreticalmodel.