Detecting differential item functioning in multidimensional graded response models with recursive partitioning

Classe, Franz ; Kern, Christoph

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URN: urn:nbn:de:bsz:180-madoc-668938
Document Type: Article
Year of publication Online: 2024
The title of a journal, publication series: Applied Psychological Measurement
Volume: tba
Issue number: tba
Page range: 1-21
Place of publication: Thousand Oaks, CA
Publishing house: Sage Publications
ISSN: 0146-6216 , 1552-3497
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 310 Statistics
Keywords (English): differential item functioning , multidimensional item response theory , graded response model , categorical analysis , surveys , algorithmic modeling , machine learning
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.

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ORCID: Classe, Franz ; Kern, Christoph ORCID: 0000-0001-7363-4299

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