Alternatives to Logistic Regression Models in Experimental Studies

• NCJ Number: 255846
• Journal: Journal of Experimental Education Dated: 2019
• Author(s): Francis L. Huang
• Date Published: 2019
• Length: 12 pages
• Grant Number(s): 2012-JF-FX-0062
• Sponsoring Agency: Office of Juvenile Justice and Delinquency Prevention (OJJDP)
• Publication Format: Article
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Language: English
• Country: United States of America

Annotation

This article presents alternatives to logistic regression models (LRMs) for the analysis of experiments and discusses the linear probability model, the log-binomial model, and the modified Poisson regression model.

Abstract

Experiments in psychology or education often use logistic regression models (LRMs) when analyzing binary outcomes; however, a challenge with LRMs is that results are generally difficult to understand. In the current project a Monte Carlo simulation assessed bias in point estimates and standard errors as well as power and Type I error rates of the different methods. Findings show that the linear probability and the modified Poisson regression models are valid, unbiased, and in some cases, better alternatives to the LRM when the predictor of interest is a binary variable. An applied example is provided. (publisher abstract modified)