Detection of Uniform and Non-Uniform Differential Item Functioning by Item Focussed Trees

TL;DR

This paper introduces a novel nonparametric method combining recursive partitioning and logistic regression to detect uniform and non-uniform differential item functioning, providing visual and interpretable results.

Contribution

It proposes an innovative tree-based approach for DIF detection that visualizes interactions and handles both categorical and continuous covariates, improving interpretability over traditional methods.

Findings

Effective detection of DIF in simulations

Visual trees reveal variable interactions in DIF

Method applicable to diverse covariate types

Abstract

Detection of differential item functioning by use of the logistic modelling approach has a long tradition. One big advantage of the approach is that it can be used to investigate non-uniform DIF as well as uniform DIF. The classical approach allows to detect DIF by distinguishing between multiple groups. We propose an alternative method that is a combination of recursive partitioning methods (or trees) and logistic regression methodology to detect uniform and non-uniform DIF in a nonparametric way. The output of the method are trees that visualize in a simple way the structure of DIF in an item showing which variables are interacting in which way when generating DIF. In addition we consider a logistic regression method in which DIF can by induced by a vector of covariates, which may include categorical but also continuous covariates. The methods are investigated in simulation studies and illustrated by two applications.