Educational and Psychological Measurement· 2013Q1
Sample Size Requirements for Structural Equation Models
- 3,610citations
- Q1SCImago
- 2013year
Short summary
Monte Carlo simulations reveal that common rules-of-thumb for sample size in structural equation modeling (SEM) are often outdated, with required sample sizes ranging from 30 to 460 cases depending on model properties.
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Key points
- Commonly used rules-of-thumb for SEM sample size are often outdated.
- Monte Carlo simulations evaluated sample size needs across various SEM properties.
- Required sample sizes ranged from 30 to 460 cases.
- Model properties like number of indicators and factor loadings significantly impact sample size needs.
AI-generated from the title and abstract; the full text is not read.
Abstract
Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad "lessons learned" for determining SEM sample size requirements are discussed.
The authors' abstract, as published at the source. Educational and Psychological Measurement, 2013 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences