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

2013/12/18 by Carolin Strobl, Julia Kopf, Achim Zeileis · 165 citations
Computer Science · Mathematics · Psychology · #Advanced Statistical Modeling Techniques #Artificial intelligence #Class (philosophy) #Cognitive Abilities and Testing #Computer science #Covariate #Differential item functioning #Interpretation (philosophy) #Item response theory #Machine learning #Mathematics #Mental Health Research Topics #Polytomous Rasch model #Psychometrics #Rasch model #Recursive partitioning #Statistical model #Statistics

paper · pdf · doi:10.1007/s11336-013-9388-3

published in Psychometrika 80(2), 289-316 (Springer Science+Business Media)

openalex publication_date 2013/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Rasch Model for Partial Credit Scoring

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