Strategies for testing statistical and practical significance in detecting DIF with logistic regression models

Abstract

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and third, the degree of equivalence between the main DIF classification systems. Different strategies to tests–LR models, and different DIF classification systems, were compared using data obtained from the University of Tehran English Proficiency Test (UTEPT). The data obtained from 400 test takers who hold a master’s degree in science and engineering or humanities were investigated for DIF. The data were also analyzed with the Mantel–Haenszel procedure in order to have an appropriate comparison for detecting uniform DIF. The article provides some guidelines for DIF detection using LR models that can be useful for practitioners in the field of language testing and assessment.