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When and how to use set‐exploratory structural equation modelling to test structural models: A tutorial using the R package lavaan
Open accessAbstract
Exploratory structural equation modelling (ESEM) is an alternative to the well‐known method of confirmatory factor analysis (CFA). ESEM is mainly used to assess the quality of measurement models of common factors but can be efficiently extended to test structural models. However, ESEM may not be the best option in some model specifications, especially when structural models are involved, because the full flexibility of ESEM could result in technical difficulties in model estimation. Thus, set‐ESEM was developed to accommodate the balance between full‐ESEM and CFA. In the present paper, we show examples where set‐ESEM should be used rather than full‐ESEM. Rather than relying on a simulation study, we provide two applied examples using real data that are included in the OSF repository. Additionally, we provide the code needed to run set‐ESEM in the free R package lavaan to make the paper practical. Set‐ESEM structural models outperform their CFA‐based counterparts in terms of goodness of fit and realistic factor correlation, and hence path coefficients in the two empirical examples. In several instances, effects that were non‐significant (i.e., attenuated) in the CFA‐based structural model become larger and significant in the set‐ESEM structural model, suggesting that set‐ESEM models may generate more accurate model parameters and, hence, lower Type II error rate.
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Smaller AIC values may indicate better approximations of the true measurement model after accounting for model complexity.
“While there are no comparable criteria for information -based indices like the AIC, smaller values may indicate better approximations of the true measurement model after accounting for model complexity (Marsh and Alamer 2024).”
From Beyond Primary Assessment on the WISC-V: An Investigation of the Structural Validity of the Ancillary Scores (2025) · cites this paper as Marsh and Alamer 2024
Sections 3.2.3 and 3.3.2 (Results; page unavailable) say that lower AIC values suggest better fit for the set-ESEM-based model. They compare structural models, however, and do not explicitly claim that AIC identifies a better approximation of the true measurement model after accounting for model complexity. (LLM, conf=0.86)
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Metadata
- Authors
- Marsh, H., & Alamer, A.
- Affiliations
- Institute of Positive Psychology and Education Australian Catholic University Sydney New South Wales Australia
- UNSW Sydney (AU)
- Corresponding author
- Alamer, Abdullah
- ORCID
- Alamer, Abdullah
- Venue
- British Journal of Mathematical and Statistical Psychology
- Publisher
- Wiley
- Published
- 2024-02-15
- Volume
- 77
- Issue
- 3
- Pages
- 459-476
- ISSN
- 0007-1102 (ISSN-L)
- 2044-8317
- OpenAlex
- W4391881574
- Cited by
- 61
- References
- 1
- Citations by year
- 2026: 32 · 2025: 16 · 2024: 13
- FWCI
- 15.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- British Journal of Mathematical and Statistical Psychology (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- University of New South Wales
- King Faisal University
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- exploratory structural equation modeling, confirmatory factor analysis, structural model, measurement model, factor correlation, model fit, type II error, lavaan, R package
- MeSH terms
- Latent Class Analysis, Computer Simulation, Data Interpretation, Statistical, Factor Analysis, Statistical, Humans, Psychometrics, Software, Models, Statistical
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