Improving Mantel–Haenszel DIF Estimation Through Bayesian Updating

This study demonstrates how the stability of Mantel–Haenszel (MH) DIF (differential item functioning) methods can be improved by integrating information across multiple test administrations using Bayesian updating (BU). The authors conducted a simulation that showed that this approach, which is based on earlier work by Zwick, Thayer, and Lewis, can yield more accurate DIF estimation and improve the detection of DIF items, even when compared to other approaches that aggregate data across administrations. The authors also applied the method to data from several college-level tests. The BU approach provides a natural way to accumulate all known DIF information about each test item while mitigating the undesirable bias toward zero that affected the performance of two previous Bayesian DIF methods.