Detecting Item Parameter Drift in Small Sample Rasch Equating

ABSTRACTScreening items for parameter drift helps protect against serious validity threats and ensure score comparability when equating forms. Although many high-stakes credentialing examinations operate with small sample sizes, few studies have investigated methods to detect drift in small sample equating. This study demonstrates that several newly researched drift detection strategies can improve equating accuracy under certain conditions with small samples where some anchor items display item parameter drift. Results showed that the recently proposed methods mINFIT and mOUTFIT as well as the more conventional Robust-z helped mitigate the adverse effects of drifting anchor items in conditions with higher drift levels or with more than 75 examinees. In contrast, the Logit Difference approach excessively removed invariant anchor items. The discussion provides recommendations on how practitioners working with small samples can use the results to make more informed decisions regarding item parameter drift. Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/08957347.2023.2274567Notes1 In certain testing designs, some items may be reused as non-anchor items on future forms. Although IPD can occur on those items, we use the traditional IPD definition as specific to differential functioning in the items reused to serve as the equating anchor set.2 In IRT, the old form anchor item parameter estimates can also come from a pre-calibrated bank. However, we use the old and new form terminology as the simulation design involves directly equating to a previous form.3 For example, assume an item drifted in the 1.0 magnitude condition from b = 0 to 1 between Forms 1 and 2, this item would be treated as having a true b of 1.0 if selected on the Form 3.