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Principles and practice in reporting structural equation analyses.

McDonald, Roderick P., Ho, Moon-Ho Ringo·2002·Psychological Methods

DOI 10.1037/1082-989x.7.1.64

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Abstract

Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.

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Authors: McDonald, R. P., & Ho, M.-H. R.
Affiliations: University of Illinois Urbana-Champaign (US)
ORCID: Ho, Moon-Ho Ringo
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2002
Volume: 7
Issue: 1
Pages: 64-82
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/1082-989x.7.1.64
OpenAlex: W2134368355
Cited by: 4,734
References: 102
Citations by year: 2026: 176 · 2025: 300 · 2024: 313 · 2023: 336 · 2022: 348 · 2021: 323 · 2020: 327 · 2019: 289 · 2018: 236 · 2017: 236 · 2016: 206 · 2015: 202 · 2014: 198 · 2013: 208 · 2012: 185
FWCI: 58.65
Type: journal article
Language: en
Open access: closed
Locations: Psychological Methods (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: structural equation modeling, reporting guidelines, model justification, model identifiability, non-normality, missing data, goodness-of-fit indices, standard errors, correlation matrix, model mismatch, critical appraisal
MeSH terms: Humans, Models, Psychological, Research Design, Linear Models

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Principles and practice in reporting structural equation analyses.

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