Detecting Differential Item Functioning in the Rasch Model by Means of Recursive Partitioning

Differential item functioning (DIF) can lead to an unfair advantage or disadvantage for certain subgroups in educational and psychological testing. A variety of statistical methods has been suggested for detecting DIF in the Rasch model. Most of these methods are designed for the comparison of pre-specified focal and reference groups, such as males and females. Latent class approaches, on the other hand, allow to detect previously unknown groups exhibiting DIF but provide no straightforward interpretation of the groups. We propose a new method for DIF detection based on model-based recursive partitioning, that can be considered as a compromise between those two extremes. With this approach it is possible to detect groups of subjects exhibiting DIF, which are not pre- specified, but result from combinations of observed covariates in a data-driven way. These groups are directly interpretable and can thus help understand the psychological sources of DIF. The talk outlines the statistical methodology behind the new approach as well as its practical application.