To detect differential item functioning (DIF), Rasch trees search for optimal split-points in covariates and identify subgroups of respondents in a data-driven way. To determine whether and in which covariate a split should be performed, Rasch trees use statistical significance tests. Consequently, Rasch trees are more likely to label small DIF…

The early detection of item drift is an important issue for frequently administered testing programs because items are reused over time. Unfortunately, operational data tend to be very sparse and do not lend themselves to frequent monitoring analyses, particularly for on-demand testing. Building on existing residual analyses, the authors propose…

This study adapted an effect size measure used for studying differential item functioning (DIF) in unidimensional tests and extended the measure to multidimensional tests. Two effect size measures were considered in a multidimensional item response theory model: signed weighted P-difference and unsigned weighted P-difference. The performance of…

This study is an evaluation of the behavior of the Liu-Agresti estimator of the cumulative common odds ratio when identifying differential item functioning (DIF) with polytomously scored test items using small samples. The Liu-Agresti estimator has been proposed by Penfield and Algina as a promising approach for the study of polytomous DIF but no…

The development and evaluation of methods for detecting potentially biased items or differentially functioning items (DIF) represent a critical area of research for psychometricians because of the negative impact of biased items on test validity. A summary is provided of the authors' 12 years of research at the University of Massachusetts…