An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression. (December 2016)

• Record Type: Journal Article
• Authors: Weiss, Brandi A.; Dardick, William
• Is Part Of: Educational and psychological measurement. Volume 76:Number 6(2016:Dec.)
• Page Start: 986
• Page End: 1004
• Publication Date: 2016-12
• Subjects: data–model fit -- logistic regression -- classification; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/0013164415623820
• Languages: English
• ISSNs: 0013-1644

Abstract

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories.