Evidence map›Paper›PMID 40021525›Full record

ReviewQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2025

Examining differential item functioning in self-reported health survey data: via multilevel modeling.

Dandan Chen Kaptur, Yiqing Liu, Bradley Kaptur, Nicholas Peterman, Jinming Zhang, Justin L Kern, Carolyn Anderson

Abstract readReview
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In one paragraph

Review in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Dandan Chen KapturPearson, Bloomington, MN, USA. danielle.chen@pearson.com.ORCID http://orcid.org/0000-0002-4020-3422
Yiqing LiuStanford University, Stanford, CA, USA.
Bradley KapturHSHS Saint John'S Hospital, Springfield, IL, USA.ORCID http://orcid.org/0000-0003-4142-0231
Nicholas PetermanCarle Illinois College of Medicine, Urbana, IL, USA.
Jinming ZhangUniversity of Illinois Urbana-Champaign, Champaign, IL, USA.
Justin L KernUniversity of Illinois Urbana-Champaign, Champaign, IL, USA.
Carolyn AndersonUniversity of Illinois Urbana-Champaign, Champaign, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Health SurveysModels, StatisticalMultilevel AnalysisSelf ReportFemaleHumansLogistic ModelsMaleMiddle AgedPsychometricsQuality of LifeDepressionDifferential item functioningHealth disparityMeasurement equivalenceMultilevel modelingPopulation density

Identifiers

PMID40021525

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.