Impact of Missing Data on the Detection of Differential Item Functioning The Case of Mantel-Haenszel and Logistic Regression Analysis

This article describes the results of a simulation study to investigate the impact of missing data on the detection of differential item functioning (DIF). Specifically, it investigates how four methods for dealing with missing data (listwise deletion, zero imputation, two-way imputation, response function imputation) interact with two methods of DIF detection (Mantel-Haenszel statistic, logistic regression analysis) under three mechanisms of missingness (data missing completely at random, data missing at random, and data missing not at random) to produce over-or underestimates of the DIF effect sizes and detection rates. Results show that the interaction effects between missingness mechanism, treatment, and rate are most influential for explaining variation in bias, root mean square errors, and rejection rates. An incorrect treatment of missing data can thus lead to severe increases of Type I and Type II error rates. However, the choice between the two DIF detection methods investigated in this study is not important.