Such rules are problematic because they are not model-specific and may lead to grossly over-or underestimated sample size requirements. Despite this, various rules-of-thumb have been advanced, including (a) a minimum sample size of 100 or 200 ( Boomsma, 1982, 1985), (b) 5 or 10 observations per estimated parameter ( Bentler & Chou, 1987 see also Bollen, 1989), and (c) 10 cases per variable ( Nunnally, 1967). However, these features of SEM also make it difficult to develop generalized guidelines regarding sample size requirements ( MacCallum, Widaman, Zhang, & Hong, 1999).
One of the strengths of SEM is its flexibility, which permits examination of complex associations, use of various types of data (e.g., categorical, dimensional, censored, count variables), and comparisons across alternative models. Advances in approaches to statistical modeling and in the ease of use of related software programs has contributed not only to an increasing number of studies using latent variable analyses but also raises questions about how to estimate the requisite sample size for testing such models. The broad “lessons learned” for determining SEM sample size requirements are discussed.ĭetermining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. 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. 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. 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.
This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs.
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in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers.
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