Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling
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.
Cite
@article{arxiv.2408.13702,
title = {Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling},
author = {Dandan Chen Kaptur and Yiqing Liu and Bradley Kaptur and Nicholas Peterman and Jinming Zhang and Justin Kern and Carolyn Anderson},
journal= {arXiv preprint arXiv:2408.13702},
year = {2025}
}
Comments
This is a preprint. The Version of Record of this article is published in Quality of Life Research (2025), and is available online at https://link.springer.com/article/10.1007/s11136-025-03936-9