A Comparison of the LR and DFIT Frameworks of Differential Functioning Applied to the Generalized Graded Unfolding Model

Abstract

Recently, applied psychological measurement researchers have become interested in the application of the generalized graded unfolding model (GGUM), a parametric item response theory model that posits an ideal point conception of the relationship between latent attributes and observed item responses. Little attention has been given to considerations for the detection of differential item functioning (DIF) under the GGUM. In this article, the authors present a Monte Carlo simulation meant to assess the efficacy of the likelihood ratio (LR) and differential functioning of items and tests (DFIT) frameworks, two popular ways of detecting DIF. Findings indicate a marked superiority of the LR approach over DFIT in terms of true and false positive rates under the GGUM. The discussion centers on possible explanations for the poor performance of the DFIT framework in detecting DIF under the GGUM and addresses limitations of the current study as well as future research directions.