A Comparison of Strategies for Estimating Conditional DIF

Abstract

In this study, the accuracies of four strategies were compared for estimating conditional differential item functioning (DIF), including raw data, logistic regression, log-linear models, and kernel smoothing. Real data simulations were used to evaluate the estimation strategies across six items, DIF and No DIF situations, and four sample size combinations for the reference and focal group data. Results showed that logistic regression was the most recommended strategy in terms of the bias and variability of its estimates. The log-linear models strategy had flexibility advantages, but these advantages only offset the greater variability of its estimates when sample sizes were large. Kernel smoothing was the least accurate of the considered strategies due to estimation problems when the reference and focal groups differed in overall ability.