Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning

Author:

Classe Franz1ORCID,Kern Christoph2

Affiliation:

1. Deutsches Jugendinstitut, Munchen, Germany

2. Ludwig-Maximilians-University of Munich, Munchen, Germany

Abstract

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.

Publisher

SAGE Publications

Reference43 articles.

1. Score-Guided Structural Equation Model Trees

2. Uncovering Sociological Effect Heterogeneity Using Machine Learning

3. Brandmaier A. M., Prindle J. J., Arnold M. (2015). semtree: Recursive partitioning of structural equation models in R [Computer software manual]. CRAN. https://cran.r-project.org/web/packages/semtree/semtree.pdf

4. Theory-guided exploration with structural equation model forests.

5. Structural equation model trees.

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