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Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling

Kenny, David A., McCoach, D. Betsy·2003·Structural Equation Modeling: A Multidisciplinary Journal

DOI 10.1207/s15328007sem1003_1

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Abstract

There has been relatively little systematic investigation of the effect of the number of variables on measures of model fit in structural equation modeling. There is conflicting evidence as to whether measures of fit tend to improve or decline as more variables are added to the model. We consider 3 different types of specification error: minor factors, 2-factor models, and method errors. Using a formal method based on the noncentrality parameter (NCP), we find that root mean squared error of approximation (RMSEA) tends to improve regardless of the type of specification error and that the comparative fit index (CFI) and Tucker-Lewis Index (TLI), generally, though not always, tend to worsen as the number of variables in the model increases. The formal method that we develop can be used to investigate other measures of fit and other types of misspecification.

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Authors: Kenny, D. A., & McCoach, D. B.
Affiliations: University of Connecticut (US)
ORCID: Kenny, David A.
McCoach, D. Betsy
Venue: Structural Equation Modeling: A Multidisciplinary Journal
Publisher: Informa UK Limited
Published: 2003-07-01
Volume: 10
Issue: 3
Pages: 333-351
ISSN: 1070-5511 (ISSN-L)
1532-8007
DOI: 10.1207/s15328007sem1003_1
OpenAlex: W2034820849
Cited by: 1,231
References: 21
Citations by year: 2026: 82 · 2025: 82 · 2024: 84 · 2023: 59 · 2022: 87 · 2021: 106 · 2020: 83 · 2019: 80 · 2018: 70 · 2017: 77 · 2016: 66 · 2015: 86 · 2014: 72 · 2013: 48 · 2012: 30
FWCI: 12.08
Type: journal article
Language: en
Open access: closed
Locations: Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: model fit measures, number of variables, model misspecification, two-factor model, method error, noncentrality parameter, comparative fit index

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Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling

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