Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling
Few health-related constructs or measures have received 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 h...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
24.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Few health-related constructs or measures have received 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. While traditional DIF methods rely on single-level
models, multilevel models offer a more suitable alternative for analyzing such
data. In this article, we highlight the advantages of multilevel modeling in
DIF analysis and demonstrate how to apply the DIF framework to self-reported
health survey data using multilevel models. For demonstration, we analyze DIF
associated with population density on the probability to answer "Yes" to a
survey question on depression and reveal that multilevel models achieve better
fit and account for more variance compared to single-level models. This article
is expected to increase awareness of the usefulness of multilevel modeling for
DIF analysis and assist healthcare researchers and practitioners in improving
the understanding of self-reported health survey data validity. |
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DOI: | 10.48550/arxiv.2408.13702 |