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Reporting practices in confirmatory factor analysis: an overview and some recommendations

Jackson, Dennis L., Gillaspy, J. Arthur, Purc-Stephenson, Rebecca·2009·Psychological Methods

DOI 10.1037/a0014694

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Abstract

Reporting practices in 194 confirmatory factor analysis studies (1,409 factor models) published in American Psychological Association journals from 1998 to 2006 were reviewed and compared with established reporting guidelines. Three research questions were addressed: (a) how do actual reporting practices compare with published guidelines? (b) how do researchers report model fit in light of divergent perspectives on the use of ancillary fit indices (e.g., L.-T. Hu & P. M. Bentler, 1999; H. W. Marsh, K.-T., Hau, & Z. Wen, 2004)? and (c) are fit measures that support hypothesized models reported more often than fit measures that are less favorable? Results indicate some positive findings with respect to reporting practices including proposing multiple models a priori and near universal reporting of the chi-square significance test. However, many deficiencies were found such as lack of information regarding missing data and assessment of normality. Additionally, the authors found increases in reported values of some incremental fit statistics and no statistically significant evidence that researchers selectively report measures of fit that support their preferred model. Recommendations for reporting are summarized and a checklist is provided to help editors, reviewers, and authors improve reporting practices.

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Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Untitled work
(1998) · Cited by 36,049
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
Significance tests and goodness of fit in the analysis of covariance structures.
Bentler, P. M., & Bonett, D. G. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.88.3.588 · Cited by 20,015
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072165 · Cited by 17,475
Confirmatory factor analysis for applied research
Brown, T. A. (2007) · Choice Reviews Online · doi:10.5860/choice.44-2769 · Cited by 15,621
Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Power analysis and determination of sample size for covariance structure modeling.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.2.130 · Cited by 10,418
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669
Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003) · Department of Psychology - University of Koblenz-Landau · doi:10.23668/psycharchives.12784 · Cited by 8,975
Testing Structural Equation Models.
Clogg, C. C., Bollen, K. A., & Long, J. S. (1995) · Social Forces · doi:10.2307/2580595 · Cited by 8,246
A Reliability Coefficient for Maximum Likelihood Factor Analysis
Tucker, L. R., & Lewis, C. (1973) · Psychometrika · doi:10.1007/bf02291170 · Cited by 7,330 · cited 2×
“…on sample size: gamma hat (Steiger, 1989); RMSEA; centrality index (CI, McDonald, 1989); SRMR; Tucker–Lewis index (TLI)/nonnormed fit index (NNFI) (Tucker & Lewis, 1973; Bentler & Bonett, 1980); relative noncentrality index (RNI; McDonald & Marsh, 1990); CFI; and Bollen’s delta 2, also referred…”
Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006) · The Journal of Educational Research · doi:10.3200/joer.99.6.323-338 · Cited by 6,937 · cited 1×
“In a more qualitative study of 16 CFA applications in the Journal of Educational Research (1989 —2004), Schreiber, Nora, Stage, Barlow, and King (2006) also found inconsistent reporting of basic information.”
Background
In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem1103_2 · Cited by 6,691
Untitled work
(2005) · Cited by 6,097
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
Curran, P. J., West, S. G., & Finch, J. F. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.16 · Cited by 5,512
The relative performance of full information maximum likelihood estimation for missing data in structural equation models
Enders, C., & Bandalos, D. (2001) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0803_5 · Cited by 4,978
Principles and practice in reporting structural equation analyses.
McDonald, R. P., & Ho, M.-H. R. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.64 · Cited by 4,734 · cited 5×
“Excellent recommendations are readily available (e.g., Boomsma, 2000; Hoyle & Panter, 1995; McDonald & Ho, 2002), and we see little utility in duplicating these efforts.”
BackgroundInfluential
Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition
Byrne, B. M. (2016) · doi:10.4324/9781315757421 · Cited by 4,653
Issues and opinion on structural equation modeling
Chin, W. W. (1998) · Journal of the Association for Information Systems · doi:10.5555/290231.290235 · Cited by 4,579
Book Review: Structural Equation Modeling: Concepts, Issues, and Applications
Rigdon, E. E., & Hoyle, R. H. (1997) · Journal of Marketing Research · doi:10.2307/3151904 · Cited by 4,088
Scale Development Research: A Content Analysis and Recommendations for Best Practices
Worthington, R. L., & Whittaker, T. A. (2006) · The Counseling Psychologist · doi:10.1177/0011000006288127 · Cited by 4,087
Factor Analysis and AIC
Akaike, H. (1987) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_29 · Cited by 4,027
Exploratory and confirmatory factor analysis: Understanding concepts and applications.
Thompson, B. (2004) · doi:10.1037/10694-000 · Cited by 3,469
An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.
Flora, D. B., & Curran, P. J. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.466 · Cited by 3,105
Structural equation modelling: Adjudging model fit
Barrett, P. T. (2006) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.018 · Cited by 2,640
Applications of Structural Equation Modeling in Psychological Research
MacCallum, R. C., & Austin, J. T. (2000) · Annual Review of Psychology · doi:10.1146/annurev.psych.51.1.201 · Cited by 2,634
Applications of structural equation modeling in marketing and consumer research: A review
Baumgartner, H., & Homburg, C. (1996) · International Journal of Research in Marketing · doi:10.1016/0167-8116(95)00038-0 · Cited by 2,585
Statistically based tests for the number of common factors
Steiger, J. H. (1980) · Medical Entomology and Zoology · Cited by 2,413
Corrections to test statistics and standard errors in covariance structure analysis.
Satorra, A., & Bentler, P. M. (1994) · Cited by 2,388
How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
Muthén, L. K., & Muthén, B. O. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0904_8 · Cited by 2,362
Understanding the limitations of global fit assessment in structural equation modeling
Steiger, J. H. (2007) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.017 · Cited by 2,360
Untitled work
(1993) · Cited by 2,269
Latent variable models: An introduction to factor, path, and structural analysis
John, C. (1987) · Medical Entomology and Zoology · Cited by 2,120
Asymptotically distribution-free methods for the analysis of covariance structures.
Browne, M. (1984) · British Journal of Mathematical & Statistical Psychology · doi:10.1111/j.2044-8317.1984.tb00789.x · Cited by 1,890
Structural Equation Modeling: Foundations and Extensions
Kaplan, D. E. (2000) · University of Maribor digital library (University of Maribor) · Cited by 1,814
Untitled work
(1996) · Dialnet (Universidad de la Rioja) · Cited by 1,733
Writing about structural equation models.
Hoyle, R. H., & Panter, A. T. (1995) · Cited by 1,708
Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes
Fan, X., Thompson, B., & Wang, L. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540119 · Cited by 1,664
Can test statistics in covariance structure analysis be trusted?
Hu, L.-T., Bentler, P., & Kano, Y. (1992) · Psychological bulletin · doi:10.1037/0033-2909.112.2.351 · Cited by 1,568
Choosing a multivariate model: Noncentrality and goodness of fit.
McDonald, R. P., & Marsh, H. W. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.247 · Cited by 1,485
The Effects of Item Parceling on Goodness-of-Fit and Parameter Estimate Bias in Structural Equation Modeling
Bandalos, D. L. (2002) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0901_5 · Cited by 1,387
Advanced Structural Equation Modeling
(2013) · Psychology Press eBooks · doi:10.4324/9781315827414 · Cited by 1,343
Structural Equation Modeling with LISREL: Essentials and Advances.
Land, K. C., & Hayduk, L. A. (1990) · Social Forces · doi:10.2307/2579660 · Cited by 1,313
Is More Ever Too Much? The Number of Indicators per Factor in Confirmatory Factor Analysis
Marsh, H. W., Hau, K.-T., Balla, J. R., & Grayson, D. (1998) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3302_1 · Cited by 1,307
A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research
Medsker, G. J., Williams, L. J., & Holahan, P. J. (1994) · Journal of Management · doi:10.1016/0149-2063(94)90022-1 · Cited by 971
In search of underlying dimensions: The use (and abuse) of factor analysis in Personality and Social Psychology Bulletin
Russell, D. W. (2002) · Personality and Social Psychology Bulletin · doi:10.1177/014616702237645 · Cited by 954
The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.
Enders, C. K. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.352 · Cited by 942
Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling
Nevitt, J., & Hancock, G. R. (2001) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem0803_2 · Cited by 886
On tests and indices for evaluating structural models
Bentler, P. M. (2006) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.024 · Cited by 858
Specification searches in covariance structure modeling.
MacCallum, R. C. (1986) · Psychological Bulletin · doi:10.1037/0033-2909.100.1.107 · Cited by 855
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis
Loehlin, J. C. (2003) · Cited by 852
Missing Data: A Gentle Introduction
McKnight, P. E., McKnight, K., Sidani, S., & Figueredo, A. J. (2007) · Cited by 782
Scaled test statistics and robust standard errors for non‐normal data in covariance structure analysis: A Monte Carlo study
Chou, C., Bentler, P. M., & Satorra, A. (1991) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1991.tb00966.x · Cited by 666
Reporting Analyses of Covariance Structures
Boomsma, A. (2000) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0703_6 · Cited by 655
Applications of covariance structure modeling in psychology: Cause for concern?
Breckler, S. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.260 · Cited by 647
Analysis of correlation matrices using covariance structure models.
Cudeck, R. A. (1989) · Psychological Bulletin · doi:10.1037/0033-2909.105.2.317 · Cited by 567
An evaluation of incremental fit indices: A clarification of mathematical and empirical properties.
Marsh, H. W., Balla, J. R., & Hau, K. (1996) · Dialnet (Universidad de la Rioja) · Cited by 558
Analysis of linear structural relationships by maximum likelihood and least squares methods
Jöreskog, K. G., & Sörbom, D. (1983) · Medical Entomology and Zoology · Cited by 447
Fit Indices Versus Test Statistics
Yuan, K.-H. (2005) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr4001_5 · Cited by 415
Sensitivity of Fit Indexes to Misspecified Structural or Measurement Model Components: Rationale of Two-Index Strategy Revisited
Fan, X., & Sivo, S. A. (2005) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem1203_1 · Cited by 410
Sample Size and Bentler and Bonett's Nonnormed Fit Index
Bollen, K. A. (1986) · Psychometrika · doi:10.1007/bf02294061 · Cited by 390
Using Confirmatory Factor Analysis for Construct Validation: An Empirical Review
DiStefano, C., & Hess, B. (2005) · Journal of Psychoeducational Assessment · doi:10.1177/073428290502300303 · Cited by 385
Use of causal models in marketing research: A review
Hulland, J. S., Chow, Y. H., & Lam, S. Y. (1996) · International Journal of Research in Marketing · doi:10.1016/0167-8116(96)00002-x · Cited by 364
A time and a place for incremental fit indices
Miles, J. N. V., & Shevlin, M. E. (2006) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.022 · Cited by 353
Model specification: Procedures, strategies, and related issues.
MacCallum, R. C. (1995) · Cited by 353
The Use of Structural Equation Modeling in Counseling Psychology Research
Martens, M. P. (2005) · The Counseling Psychologist · doi:10.1177/0011000004272260 · Cited by 343
Reporting structural equation modeling results in Psychology and Aging: Some proposed guidelines.
Raykov, T., Tomer, A., & Nesselroade, J. R. (1991) · Psychology and Aging · doi:10.1037/0882-7974.6.4.499 · Cited by 336
An index of goodness-of-fit based on noncentrality
McDonald, R. P. (1989) · Journal of Classification · doi:10.1007/bf01908590 · Cited by 312
The Impact of Categorization With Confirmatory Factor Analysis
DiStefano, C. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0903_2 · Cited by 276
The golden rule is that there are no golden rules: A commentary on Paul Barrett’s recommendations for reporting model fit in structural equation modelling
Markland, D. A. (2006) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.023 · Cited by 251
The Growth of Structural Equation Modeling: 1994-2001
Hershberger, S. L. (2003) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem1001_2 · Cited by 187
The Importance of Structure Coefficients in Structural Equation Modeling Confirmatory Factor Analysis
Thompson, B. (1997) · Educational and Psychological Measurement · doi:10.1177/0013164497057001001 · Cited by 180
Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods
Brown, R. (1994) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519409539983 · Cited by 152
Effects of Potential Confounding Factors on Fit Indices and Parameter Estimates for True and Misspecified SEM Models
Fan, X., & Wang, L. (1998) · Educational and Psychological Measurement · doi:10.1177/0013164498058005001 · Cited by 122
Structural equation modeling made difficult
Millsap, R. E. (2007) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.021 · Cited by 111
Driving Fast in Reverse
Steiger, J. H. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750332893 · Cited by 92
Assessing the adequacy of structural equation models: Golden rules and editorial policies
Goffin, R. D. (2006) · Personality and Individual Differences · doi:10.1016/j.paid.2006.09.019 · Cited by 87
The Effect of the Number of Observations per Parameter in Misspecified Confirmatory Factor Analytic Models
Jackson, D. L. (2007) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem1401_3 · Cited by 75
Self-citation
The Robustness of the Likelihood Ratio Chi-Square Test for Structural Equation Models: A Meta-Analysis
Powell, D., & Schafer, W. D. (2001) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986026001105 · Cited by 64
Multiple imputations in sample surveys
Rubin, D. B. (1978) · Cited by 62
Methods of psychological research
Craig, J. R. (1979) · Medical Entomology and Zoology · Cited by 44
On the growth of structural equation modeling in psychological journals
Tremblay, P. F., & Gardner, R. C. (1996) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519609540035 · Cited by 43
Series Editor's Note
Furst, P. T. (1976) · The Journal of Latin American and Caribbean Anthropology · doi:10.1525/jlca976.1.1.ii · Cited by 7

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Li, C.-H. (2016) · Behavior Research Methods · doi:10.3758/s13428-015-0619-7 · Cited by 3,031
The Performance of RMSEA in Models With Small Degrees of Freedom
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015) · Sociological Methods & Research · doi:10.1177/0049124114543236 · Cited by 2,841
Applications of structural equation modeling (SEM) in ecological studies: an updated review
Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016) · Ecological Processes · doi:10.1186/s13717-016-0063-3 · Cited by 1,640
Model fit and model selection in structural equation modeling.
West, S. G., Taylor, A. B., & Wu, W. (2012) · Cited by 1,232
Ten Steps in Scale Development and Reporting: A Guide for Researchers
Carpenter, S. (2018) · Communication Methods and Measures · doi:10.1080/19312458.2017.1396583 · Cited by 1,141

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Authors: Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R.
Affiliations: University of Windsor (CA)
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2009-03
Volume: 14
Issue: 1
Pages: 6-23
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/a0014694
OpenAlex: W2129748849
Cited by: 2,242
References: 87
Citations by year: 2026: 94 · 2025: 187 · 2024: 201 · 2023: 228 · 2022: 241 · 2021: 195 · 2020: 170 · 2019: 142 · 2018: 150 · 2017: 135 · 2016: 111 · 2015: 98 · 2014: 86 · 2013: 84 · 2012: 44
FWCI: 54.58
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
Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
Keywords: confirmatory factor analysis, reporting practices, reporting guidelines, model fit indices, selective reporting, missing data, normality testing, reporting checklist, APA journals
MeSH terms: Factor Analysis, Statistical, Humans, Mathematical Computing, Periodicals as Topic, Psychology, Publishing, Research, Research Design, Software, Reproducibility of Results, Models, Statistical, Guideline Adherence

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Reporting practices in confirmatory factor analysis: an overview and some recommendations

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