Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety
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Summary
This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs, and systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data.
- Type
- article
- Published
- 2013-07-09
- Cited by
- 3,385
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W1986234982
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:44962421
Keywords
Structural equation modeling, Sample size determination, Sample (material), Statistics, Econometrics
References
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- Type I Error and Power of Latent Mean Methods and MANOVA in Factorially Invariant and Noninvariant Latent Variable Systems
- Monte Carlo Experiments: Design and Implementation
- Statistical Power Analysis for the Behavioral Sciences (2nd ed.)
- The Relation Among Fit Indexes, Power, and Sample Size in Structural Equation Modeling
- Structural Equations with Latent Variables
- The Issue of Isopower in Power Analysis for Tests of Structural Equation Models
- The persistence of underpowered studies in psychological research: causes, consequences, and remedies.
- Evaluation of Confirmatory Factor Analytic and Structural Equation Models Using Goodness-of-Fit Indices
- Sample size for multiple regression: obtaining regression coefficients that are accurate, not simply significant.
- Measurement Model Quality, Sample Size, and Solution Propriety in Confirmatory Factor Models
- The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.
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