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arXiv · 2408.13702

Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling

Dandan Chen Kaptur·Yiqing Liu·Bradley Kaptur·Nicholas Peterman·Jinming Zhang·Justin Kern·Carolyn Anderson
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Abstract

Few health-related constructs or measures have received a critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence in self-reported health surveys, which are often hierarchical in structure. Traditional single-level DIF methods in this case fall short, making multilevel models a better alternative. We highlight the benefits of multilevel modeling for DIF analysis, when applying a health survey data set to multilevel binary logistic regression (for analyzing binary response data) and multilevel multinominal logistic regression (for analyzing polytomous response data), and comparing them with their single-level counterparts. Our findings show that multilevel models fit better and explain more variance than single-level models. This article is expected to raise awareness of multilevel modeling and help healthcare researchers and practitioners understand the use of multilevel modeling for DIF analysis.

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Dandan Chen Kaptur, Yiqing Liu, Bradley Kaptur, Nicholas Peterman, Jinming Zhang, Justin Kern, Carolyn Anderson. 2024-08-25. Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling. https://doi.org/10.1007/s11136-025-03936-9

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