Entropy-Based Measures for Person Fit in Item Response Theory.

ABSTRACT: This article introduces three new variants of entropy to detect person misfit (E_i, EM_i , and EMR_i ), 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 l_z, l_z*, U, and W. Simulation results indicated that EM_i and

SUBMITTER: Dardick WR 

PROVIDER: S-EPMC5978476 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature