4.3 Article

Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety

Journal

EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
Volume 73, Issue 6, Pages 913-934

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/0013164413495237

Keywords

structural equation modeling; confirmatory factor analysis; sample size; statistical power; Monte Carlo simulation; bias; solution propriety

Funding

  1. VA Career Development Award
  2. National Institute on Alcohol Abuse and Alcoholism [K01AA021266]

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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in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb.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.

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