Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models
Over the past 25 years a range of parametric and nonparametric methods have been developed for analyzing Differential Item Functioning (DIF). These procedures are typically performed for each item individually or for small numbers of related items. Because the analytic procedures focus on individual items, it has been difficult to pool information across items to identify potential sources of DIF analytically. In this article, we outline an approach to DIF analysis using hierarchical logistic regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of alternate explanations for DIF. The approach can also be used to improve the accuracy of DIF estimates for individual items by applying empirical Bayes techniques, with DIF-related item characteristics serving as collateral information. To illustrate the hierarchical logistic regression procedure, we use a large data set derived from recent computer-based administrations of Step 2, the clinical science component of the United States Medical Licensing Examination (USMLE(R)). Results of a small Monte Carlo study of the accuracy of the DIF estimates are also reported.