A Log-Linear Modeling Approach for Differential Item Functioning Detection in Polytomously Scored Items

Abstract

A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were manipulated. Also, the performance of LLM was compared with that of other observed score-based DIF methods, namely ordinal logistic regression, logistic discriminant function analysis, Mantel, and generalized Mantel-Haenszel, regarding their Type I error (rejection rates) and power (DIF detection rates). For the observed score matching stratification in LLM, 5 and 10 strata were used. Overall, generalized Mantel-Haenszel and LLM with 10 strata showed better performance than other methods, whereas ordinal logistic regression and Mantel showed poor performance in detecting balanced DIF where the DIF direction is opposite in the two pairs of categories and partial DIF where DIF exists only in some of the categories.