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Cited by 10,418Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceMeta-analysis and systematic reviews

Power analysis and determination of sample size for covariance structure modeling.

MacCallum, Robert C., Browne, Michael W., Sugawara, Hazuki M.·1996·Psychological Methods
DOI 10.1037/1082-989x.1.2.130
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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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Crossref · 10.1037/1082-989x.1.2.130

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1 of 1 references verified0 citation errors · 0 not checkable

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  • For moderate Ns and low complexity models, obtaining a narrow RMSEA confidence interval is more likely.

    “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

  • The width of RMSEA confidence intervals is affected by sample size and model complexity.

    “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

  • The CFI and SRMR values provided evidence of a good fitting model, but RMSEA values were in the commonly accepted range indicating mediocre fit.

    “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

  • Both hypothesized SEMs have good fit indices: CFI 0.97 and 0.963, TLI greater than 0.95, and RMSEA 0.059-0.072 for SEM 1 and 0.065-0.072 for SEM 2.

    “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

  • The 3-factor CFA model fit was acceptable, with CFI = 0.96 and RMSEA = 0.08 [CI, 0.06-0.09] after allowing error terms between "MBC meets her approval" and "MBC is appealing to her" and between "She welcomes MBC" and "She likes MBC" to correlate.

    “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]

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Authors
MacCallum, R. C., Browne, M. W., & Sugawara, H. M.
Affiliations
The Ohio State University (US)
Venue
Psychological Methods
Publisher
American Psychological Association (APA)
Published
1996-06
Volume
1
Issue
2
Pages
130-149
ISSN
  • 1082-989X (ISSN-L)
  • 1939-1463
DOI
10.1037/1082-989x.1.2.130
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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