A Review of ETS Differential Item Functioning Assessment Procedures: Flagging Rules, Minimum Sample Size Requirements, and Criterion Refinement
- Zwick, Rebecca
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- differential item functioning (DIF), test fairness, refinement, Mantel-Haenszel DIF, empirical Bayes DIF
Differential item functioning (DIF) analysis is a key component in the evaluation of the fairness and validity of educational tests. The goal of this project was to review the status of ETS DIF analysis procedures, focusing on three aspects: (a) the nature and stringency of the statistical rules used to flag items, (b) the minimum sample size requirements that are currently in place for DIF analysis, and (c) the efficacy of criterion refinement. The main findings of the review are as follows:
- The ETS C rule often displays low DIF detection rates even when samples are large.
- With improved flagging rules in place, minimum sample size requirements could probably be relaxed. In addition, updated rules for combining data across administrations could allow DIF analyses to be performed in a broader range of situations.
- Refinement of the matching criterion improves detection rates when DIF is primarily in one direction but can depress detection rates when DIF is balanced. If nothing is known about the likely pattern of DIF, refinement is advisable.
Each of these findings is discussed in detail, focusing on the case of dichotomous items.