Cited by 4,734Psychometric Methodologies and TestingMeta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

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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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Hope and resilience as protective factors linked to lower burnout among child welfare workers
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The pros and cons of remote work in relation to bullying, loneliness and work engagement: A representative study among Norwegian workers during COVID-19
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“The CFA analysis revealed that the measurement model’s χ 2 / df value of 2.539 was below the recommended value of 3.0 (Kline, 2010), which showed a good fit with the data, RMSEA value of 0.056 was smaller than 0.08 (McDonald and Ho, 2002), and the GFI of 0.926, NFI of 0.926 and CFI of 0.953 also surpassed the critical…”
ResultInfluential
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“08 [90], showing an acceptable fit [113].”
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“FIML estimates with the chi-square fit statistic, the Tucker–Lewis index (TLI), the comparative fit index (CFI), and the root mean square error of approximation (RMSEA) were used as indices of goodness of fit (Gefen et al., 2011; McDonald and Ho, 2002; Schreiber et al., 2006).”
Method
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“Themodels followed established guidelines for error covariance, and model fit was tested using comparative fit index (CFI) and root mean squared error of approximation (RMSEA) following the use of Full Information Maximum Likelihood method to impute missing values (32).”
Method
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“based on categorical data and we present the six indices which have been shown to be useful in assessing model fit [19 22].”
Method
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“Values smaller than .08 for RMSEA and .10 for SRMR, as well as values greater than .95 for CFI suggest acceptable model fit (see Hu & Bentler, 1999; Kline, 2011; McDonald & Ho, 2002).”
Method
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“The null hypothesis in case of these models is that there is no correlation between the variables (McDonald & Ho, 2002).”
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“Values of CFI and TLI higher than 0.90, SRMR and RMSEA lower than 0.08 (Fan & Sivo, 2007; Hu & Bentler, 1999; McDonald & Ho, 2002) were considered indicating acceptable fit.”
BackgroundInfluential
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“Because there is no consensus in the standards used to determine whether a model has good statistical fit to the data (McDonald and Ho 2002), we utilized several fit indices.”
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“For these second and third steps, being nested models, comparisons were made following the usual procedures in these cases through the Δχ2 (Byrne, 2010; McDonald & Ho, 2002).”
Method
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“For the evaluation of goodness of fit in our models, we relied on established recommendations [57].”
Method
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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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