Item focussed Trees for the Identification of Items in Differential Item Functioning

TL;DR

This paper introduces a novel recursive partitioning method for detecting differential item functioning (DIF) in test items, allowing for flexible covariate effects and simultaneous item and variable selection.

Contribution

It extends the Rasch model to incorporate multiple covariates and uses recursive partitioning to identify DIF, providing a new, flexible approach compared to traditional methods.

Findings

Method performs well in simulations.

Outperforms traditional DIF detection methods.

Applications demonstrate practical usefulness.

Abstract

A new method for the identification of differential item functioning (DIF) by using recursive partitioning techniques is proposed. We assume an extension of the Rasch model that allows for DIF being induced by an arbitrary number of covariates for each item. Recursive partitioning on the item level results in one tree for each item and leads to simultaneous selection of items and variables that induce DIF. For each item it is possible to detect groups of subjects with different item difficulties, defined by combinations of characteristics that are not pre-specified. An algorithm is proposed that is based on permutation tests. Various simulation studies, including the comparison with traditional approaches to identify items with DIF, show the applicability and the competitive performance of the method. Two applications illustrate the usefulness and the advantages of the new method.