The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Power analysis and determination of sample size for covariance structure modeling.
Abstract
A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes.
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In the citing paper
“On the other hand, for moderate Ns and low complexity models, obtaining a narrow confidence interval is more likely (MacCallum et al., 1996 cited in Byrne, 2012).”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as MacCallum et al., 1996 cited in Byrne, 2012
In the citing paper
“Additionally, the width of RMSEA confidence intervals is affected by sample size and model complexity (MacCallum et al., 1996; Brown, 2015; Byrne, 2012).”
From Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018) · cites this paper as MacCallum et al., 1996
In the citing paper
“Overall, the CFI and SRMR values provided evidence of a good fitting model.However, RMSEA values were in the commonly accepted range indicating mediocre fit (MacCallum et al. 1996).”
From Disclosing Extra-Dyadic Involvement (EDI): Understanding Attitudes, Subjective Norms, and Perceived Behavioral Control (2013) · cites this paper as MacCallum et al. 1996
In the citing paper
“Both hypothesized models have reasonably strong indices of fit, as indicated in Table 5. Comparative fit indices (CFIs) of 0.97 and 0.963 respectively indicate that the models exhibit good fit according to several general guidelines and recommendations (Bentler, 1990, MacCallum et al., 1996, Hu and Bentler, 1999).Similarly, the models also both have high Tucker-Lewis indices (TLIs) of greater than”
From Discriminatory attitudes between ridesharing passengers (2020) · cites this paper as MacCallum et al., 1996
In the citing paper
“This was evidenced by CFI = 0.96 and RMSEA = 0.08 [CI, 0.06–0.09] [18, 22] after allowing the error terms between “MBC meets her approval” and “MBC is appealing to her” as well as between “She welcomes MBC” and “She likes MBC” to correlate to account for differences in sentence structure (i.e., passive versus active voice)(see Fig. 2).”
From Psychometric assessment of three newly developed implementation outcome measures (2017) · cites this paper as [18]
In the citing paper
“Power analysis based on the RMSEA test of close fit estimated power at 84% with a sample size of 60 participants [18].”
From Psychometric assessment of three newly developed implementation outcome measures (2017) · cites this paper as [18]
Citations
Cites
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Self-citation
Cited by
Sorted by citations
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Evaluating the use of exploratory factor analysis in psychological research.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.3.272 · Cited by 9,587
Self-citationSchermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003) · Department of Psychology - University of Koblenz-Landau · doi:10.23668/psycharchives.12784 · Cited by 8,975
Structural Equation Modelling: Guidelines for Determining Model Fit
Hooper, D., Coughlan, J., & Mullen, M. R. (2008) · Dublin Institute of Technology · doi:10.21427/d7cf7r · Cited by 8,746
Metadata
- Authors
- MacCallum, R. C., Browne, M. W., & Sugawara, H. M.
- Affiliations
- The Ohio State University (US)
- Venue
- Psychological Methods
- Published
- 1996-06
- Volume
- 1
- Issue
- 2
- Pages
- 130-149
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W1971410477
- Cited by
- 10,418
- References
- 1
- Citations by year
- 2026: 637 · 2025: 718 · 2024: 657 · 2023: 739 · 2022: 687 · 2021: 716 · 2020: 655 · 2019: 607 · 2018: 578 · 2017: 519 · 2016: 518 · 2015: 469 · 2014: 464 · 2013: 428 · 2012: 317
- FWCI
- 35.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (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
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- power analysis, sample size determination, structural equation modeling, model fit assessment, RMSEA, effect size, hypothesis testing
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