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Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model

Published online by Cambridge University Press:  01 January 2025

Carolin Strobl ,
Julia Kopf  and
Achim Zeileis
Carolin Strobl*
Affiliation:
Universität Zürich
Julia Kopf
Affiliation:
Ludwig-Maximilians-Universität München
Achim Zeileis
Affiliation:
Universität Innsbruck
*
Requests for reprints should be sent to Carolin Strobl, Department of Psychology, Universität Zürich, Binzmühlestr. 14, 8050 Zürich, Switzerland. E-mail: Carolin.Strobl@psychologie.uzh.ch
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Abstract

A variety of statistical methods have been suggested for detecting differential item functioning (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 the detection of previously unknown groups exhibiting DIF. However, this approach provides no straightforward interpretation of the groups with respect to person characteristics. Here, 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. These groups are directly interpretable and can thus help generate hypotheses about the psychological sources of DIF. The statistical background and construction of the new method are introduced by means of an instructive example, and extensive simulation studies are presented to support and illustrate the statistical properties of the method, which is then applied to empirical data from a general knowledge quiz. A software implementation of the method is freely available in the R system for statistical computing.

Keywords

item response theoryIRTRasch modeldifferential item functioningDIFmeasurement invariancestructural changemodel-based recursive partitioning

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 2 , June 2015 , pp. 289 - 316
Copyright
Copyright © 2013 The Psychometric Society

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