Bayesian Approaches for Detecting Differential Item Functioning Using the Generalized Graded Unfolding Model

Differential item functioning (DIF) analysis is one of the most important applications of item response theory (IRT) in psychological assessment. This study examined the performance of two Bayesian DIF methods, Bayes factor (BF) and deviance information criterion (DIC), with the generalized graded unfolding model (GGUM). The Type I error and power were investigated in a Monte Carlo simulation that manipulated sample size, DIF source, DIF size, DIF location, subpopulation trait distribution, and type of baseline model. We also examined the performance of two likelihood-based methods, the likelihood ratio (LR) test and Akaike information criterion (AIC), using marginal maximum likelihood (MML) estimation for comparison with past DIF research. The results indicated that the proposed BF and DIC methods provided well-controlled Type I error and high power using a free-baseline model implementation, their performance was superior to LR and AIC in terms of Type I error rates when the reference and focal group trait distributions differed. The implications and recommendations for applied research are discussed.

Automated summary

Differential item functioning (DIF) analysis is an important application of item response theory (IRT) in psychological assessment. This study compared the performance of two Bayesian DIF methods and a generalized graded unfolding model (GGUM) through simulations and found some interesting results.