Iterative Purification and Effect Size Use With Logistic Regression for Differential Item Functioning Detection

Abstract

Two unresolved implementation issues with logistic regression (LR) for differential item functioning (DIF) detection include ability purification and effect size use. Purification is suggested to control inaccuracies in DIF detection as a result of DIF items in the ability estimate. Additionally, effect size use may be beneficial in controlling Type I error rates. The effectiveness of such controls, especially used in combination, requires evaluation. Detection errors were evaluated through simulation across iterative purification and no purification procedures with and without the use of an effect size criterion. Sample size, DIF magnitude and percentage, and ability differences were manipulated. Purification was beneficial under certain conditions, although overall power and Type I error rates did not substantially improve. The LR statistical test without purification performed as well as other classification criteria and may be the practical choice for many situations. Continued evaluation of the effect size guidelines and purification are discussed.