Cited by 61Psychometric Methodologies and TestingMental Health Research TopicsStatistical Methods and Bayesian Inference

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

When and how to use set‐exploratory structural equation modelling to test structural models: A tutorial using the R package lavaan

Open access
Marsh, Herb, Alamer, Abdullah·2024·British Journal of Mathematical and Statistical Psychology

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/bmsp.12336

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 43

  1. 10.3389/fpsyg.2021.732016 · DOI
  2. 10.1080/01443410.2020.1867076 · DOI
  3. 10.1016/j.rmal.2022.100005 · DOI
  4. 10.1080/09588221.2021.1903042 · DOI
  5. 10.1111/jcal.12753 · DOI
  6. 10.1093/applin/amac035 · DOI
  7. 10.1017/S0272263121000863 · DOI
  8. 10.1016/j.actpsy.2023.104021 · DOI
  9. 10.1080/10705510903008204 · DOI
  10. 10.1080/00273171.2014.933697 · DOI
  11. 10.1080/00273171.2012.715252 · DOI
  12. 10.4324/9781315779553 · DOI · Paper page
  13. 10.4324/9781351006743 · DOI
  14. 10.1080/01434632.2022.2075002 · DOI
  15. Faraci P., 2023, A new short version of Internet Gaming Disorder‐20 (IGD‐20): An exploratory structural equation modeling, Clinical Neuropsychiatry, 20, 205
  16. 10.3389/fpsyg.2022.1037111 · DOI
  17. 10.1080/00220973.2013.876231 · DOI
  18. 10.1037/edu0000664 · DOI
  19. 10.1080/10705519909540118 · DOI · Paper page
  20. 10.1016/j.rmal.2022.100038 · DOI
  21. 10.3389/fpsyg.2022.971495 · DOI
  22. 10.1353/csd.2004.0059 · DOI
  23. 10.1080/10705511.2018.1444993 · DOI
  24. 10.1080/00273171.2019.1602503 · DOI
  25. 10.1080/00220973.1996.10806604 · DOI
  26. 10.1146/annurev-clinpsy-032813-153700 · DOI
  27. 10.1080/10705510903008220 · DOI · Paper page
  28. Morin A. J., 2023, Handbook of structural equation modeling
  29. Morin A. J., 2013, Structural equation modeling: A second course, 395
  30. 10.1093/oxfordhb/9780197600047.013.31 · DOI
  31. Ostrowska S., 2021, Unlock
  32. 10.18637/jss.v048.i02 · DOI · Paper page
  33. 10.1521/978.14625/28806 · DOI · Paper page
  34. 10.3389/fpsyg.2022.901412 · DOI
  35. 10.2307/1412107 · DOI · Paper page
  36. 10.1016/j.bodyim.2023.101641 · DOI
  37. Tabachnick B. G., 2023, Using multivariate statistics
  38. Thurstone L. L., 1935, The vectors of the mind
  39. Thurstone L. L., 1947, Multiple factor analysis
  40. 10.1093/acprof:oso/9780199777600.001.0001 · DOI · Paper page
  41. 10.3389/fpsyt.2021.795672 · DOI
  42. 10.1111/j.1540-4781.2006.00424.x · DOI
  43. 10.1111/0081‐1750.00078 · DOI

Cited by

Sorted by citations

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
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review