A Comparison of Logistic Regression Models for DIF Detection in Polytomous Items: The Effect of Small Sample Sizes and Non-Normality of Ability Distributions

This study investigated the effectiveness of logistic regression models to detect uniform and non-uniform DIF in polytomous items across small sample sizes and non-normality of ability distributions. A simulation study was used to compare three logistic regression models, which were the cumulative logits model, the continuation ratio model, and the adjacent categories model. The results revealed that logistic regression was a powerful method to detect DIF in polytomous items, but not useful to distinguish the type of DIF. Continuation ratio model worked best to detect uniform DIF, but the cumulative logits model gave more acceptable type I error results. As sample size increased, type I errors increased at cumulative logits model results. Skewness of ability distributions reduced power of logistic regression to detect non-uniform DIF. Small sample sizes reduced power of logistic regression.