Refining effect size measures and classification for differential item functioning: Toward unified guidelines across...

Differential Item Functioning (DIF) analysis is used to identify potentially biased items in multi-item measurements. In addition to testing the statistical significance, it is essential to evaluate the practical significance of DIF through effect size measures. We review existing DIF effect size measures and cut-off values used to classify the effect size magnitudes for the Mantel-Haenszel test, SIBTEST, and logistic regression for binary items, and introduce a refinement of area-based effect size measures. A simulation study is conducted to investigate the properties of these effect size measures and existing classification guidelines, and to assess their comparative performance. The results indicate that some commonly used effect size measures exhibit undesirable properties, including inconsistent classifications, systematic underestimation of the magnitude of the underlying DIF, and strong dependence on design factors. To address these issues, we introduce usage restrictions for some effect size measures, revised cut-off values that unify results across different methods, and propose new cut-off values for area-based effect size measures. The methods are demonstrated using two real data examples. Implementation is provided in the R software.