A Comparison of Logistic Regression and Analysis of Variance Differential Item Functioning Detection Methods

Abstract

Differential item functioning (DIF) detection rates were compared between logistic regression and analysis of variance for dichotomously scored items. These two DIF methods were compared using simulated binary item response data sets of varying test length (20, 40, and 60 items), sample size (200, 400, and 600 examinees), discrimination type (fixed and varying), and relative underlying ability (equal and unequal) between groups under conditions of uniform DIF, nonuniform DIF, combination DIF, and false positive errors. These test conditions were replicated 100 times. For both DIF detection methods, a test length of 20 items was sufficient for satisfactory DIF detection with detection rate increasing as sample size increased. With the exception of uniform DIF, the logistic regression method had higher mean detection rates than the analysis of variance method. Because the type of DIF present in real data is rarely known, the logistic regression method is recommended for most practical applications.