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

TL;DR

This paper advocates for using multilevel modeling techniques to analyze differential item functioning in hierarchical self-reported health survey data, demonstrating their superiority over traditional single-level methods.

Contribution

It introduces multilevel models for DIF analysis in health surveys and compares their effectiveness with single-level models, highlighting improved fit and variance explanation.

Findings

Multilevel models fit data better than single-level models.

Multilevel models explain more variance in responses.

Multilevel modeling enhances DIF analysis in hierarchical data.

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.