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Published on Leave a comment on Article Published: An Investigation of Chi-square and Entropy-Based Methods of Item-Fit Using Item-Level Contamination in Item Response Theory

Dr. William Dardick and Dr. Brandi Weiss published an article titled, "An investigation of chi-square and entropy-based methods of item-fit using item-level contamination in item response theory" in the Journal of Modern Applied Statistical Methods, 18(2), EP3208. (DOI: 10.22237/jmasm/1604190480).

Abstract

New variants of entropy as measures of item-fit in item response theory are investigated. Monte Carlo simulation(s) examine aberrant conditions of item-level misfit to evaluate relative (compare EMRj, X2, G2, S-X2, and PV-Q1) and absolute (Type I error and empirical power) performance. EMRj has utility in discovering misfit.  

(Publication Date: October 2, 2020)

Keywords: item response theory, IRT item-fit, IRT model fit, Monte Carlo simulation.

Published on Leave a comment on Article Published: Separating the Odds: Thresholds for Entropy in Logistic Regression

Drs. Brandi A. Weiss and William Dardick published an article titled, "Separating the odds: Thresholds for entropy in logistic regression" in the Journal of Experimental Education (DOI: 10.1080/00220973.2019.1587735).

Abstract

Researchers are often reluctant to rely on classification rates because a model with favorable classification rates but poor separation may not replicate well. In comparison, entropy captures information about borderline cases unlikely to generalize to the population. In logistic regression, the correctness of predicted group membership is known, however, this information has not yet been utilized in entropy calculations. The purpose of this study was to, 1) introduce three new variants of entropy as approximate-model-fit measures, 2) establish rule-of-thumb thresholds to determine whether a theoretical model fits the data, and 3) investigate empirical Type I error and statistical power associated with those thresholds. Results are presented from two Monte Carlo simulations. Simulation results indicated that EFR-rescaled was the most representative of overall model effect size, whereas EFR provided the most intuitive interpretation for all group size ratios. Empirically-derived thresholds are provided.

Keywords: Classification, cut-point methods, entropy, logistic regression, model-fit, misclassification

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