A more general model for testing measurement invariance and differential item functioning

Bauer, Daniel J.

doi:10.1037/met0000077

Cited by 363Psychometric Methodologies and TestingEducational Assessment and PedagogyStatistical Methods and Bayesian Inference

A more general model for testing measurement invariance and differential item functioning

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Bauer, Daniel J.·2017·Psychological Methods

DOI 10.1037/met0000077PMC PMC5140785

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Abstract

The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable modeling approaches: the multiple groups model or the multiple-indicator multiple-cause (MIMIC) model. Both approaches offer opportunities to detect differential item functioning within multi-item scales, and thereby to test measurement invariance, but both approaches also have significant limitations. The multiple groups model allows 1 to examine the invariance of all model parameters but only across levels of a single categorical individual difference variable (e.g., ethnicity). In contrast, the MIMIC model permits both categorical and continuous individual difference variables (e.g., sex and age) but permits only a subset of the model parameters to vary as a function of these characteristics. The current article argues that moderated nonlinear factor analysis (MNLFA) constitutes an alternative, more flexible model for evaluating measurement invariance and differential item functioning. We show that the MNLFA subsumes and combines the strengths of the multiple group and MIMIC models, allowing for a full and simultaneous assessment of measurement invariance and differential item functioning across multiple categorical and/or continuous individual difference variables. The relationships between the MNLFA model and the multiple groups and MIMIC models are shown mathematically and via an empirical demonstration. (PsycINFO Database Record

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A PTSD severity score was created from clinical interviews and self-report measures using an Integrative Data Analysis/Moderated NonLinear Factor Analysis framework.
“Using an Integrative Data Analysis/Moderated NonLinear Factor Analysis (IDA/MNLFA) framework (Bauer, 2017; Hussong et al., 2020), a PTSD severity score was created from clinical interviews and self-report measures used to assess PTSD in the individual parent studies (Hien et al., 2022).”
From Cannabis use and trauma-focused treatment for co-occurring posttraumatic stress disorder and substance use disorders: A meta-analysis of individual patient data (2024) · cites this paper as Bauer, 2017

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Authors: Bauer, D. J.
Affiliations: University of North Carolina at Chapel Hill (US)
Corresponding author: Bauer, Daniel J.
ORCID: Bauer, Daniel J.
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2017-09
Volume: 22
Issue: 3
Pages: 507-526
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/met0000077
PMC: PMC5140785
OpenAlex: W2420991929
Cited by: 363
References: 20
Citations by year: 2026: 57 · 2025: 54 · 2024: 52 · 2023: 37 · 2022: 52 · 2021: 34 · 2020: 31 · 2019: 16 · 2018: 19 · 2017: 9 · 2016: 2
FWCI: 16.81
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
www.apa.org/pubs/journals/resources/open-access.aspx (am)
Locations: Psychological Methods (publishedVersion, other-oa)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (R01 DA034636)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Social Sciences › Education › Educational Assessment and Pedagogy
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: measurement invariance, differential item functioning, moderated nonlinear factor analysis, latent variable models, measurement validity
MeSH terms: Data Collection, Data Interpretation, Statistical, Factor Analysis, Statistical, Humans, Psychometrics, Models, Statistical

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A more general model for testing measurement invariance and differential item functioning

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