Volume 74, Issue 1 e12592
ORIGINAL ARTICLE

A literature review of model fit and model comparisons with confirmatory factor analysis: Formalizing the informal in organizational science

Matt C. Howard

Corresponding Author

Matt C. Howard

Mitchell College of Business, University of South Alabama, Mobile, Alabama, USA

Correspondence

Matt C. Howard, Mitchell College of Business, University of South Alabama, 5811 USA Drive S., Rm. 337, Mobile, AL 36688, USA.

Email: [email protected]

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Melanie Boudreaux

Melanie Boudreaux

Al Danos College of Business Administration, Nicholls State University, Thibodaux, Louisiana, USA

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Joshua Cogswell

Joshua Cogswell

Al Danos College of Business Administration, Nicholls State University, Thibodaux, Louisiana, USA

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Kelly G. Manix

Kelly G. Manix

Jennings A. Jones College of Business, Middle Tennessee State University, Murfreesboro, Tennessee, USA

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Matthew T. Oglesby

Matthew T. Oglesby

Sanders College of Business and Technology, University of North Alabama, Florence, Alabama, USA

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First published: 03 December 2024

Joshua Cogswell, Kelly Manix, and Matthew Oglesby contributed equally to the manuscript, and their names appear in alphabetical order.

We would like to thank Jeffrey Lovelace, Andrew Hanna, Janaki Gooty, and especially Brett Neely for their feedback on an earlier version of the current article.

Funding Information: No funding was received in association with the current wor

Abstract

Researchers often stray from recommendations provided by simulation studies when conducting confirmatory factor analysis (CFA), causing unwieldy applications of the analysis and diminished confidence in published results. We introduce three particularly important informal practices associated with (1) alternative interpretations of model fit, (2) the use of inadvisable combinations of fit indices, and (3) the failure to conduct effective model comparisons. We then review over 2000 CFAs in premier organizational science journals. Our results support that researchers widely engage in all three informal practices. To address this tension, we (1) formalize modern interpretations of model fit by providing percentile ranges of indices in published articles, such that researchers can make relative and continuous assessments of model fit. We (2) emphasize the importance of assessing multiple recommended fit indices together to provide complete depictions of model soundness. Lastly, we (3) demonstrate the necessity to perform appropriate model comparisons, including the assessment of more complex models.

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest to disclose.

DATA AVAILABILITY STATEMENT

All data associated with the current article are provided in the supplemental materials.