title: Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety.
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in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb.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.
authors.affiliation: National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA ; Boston University School of Medicine, Boston, MA, USA.
authors.forename: Erika J
authors.initials: EJ
authors.lastname: Wolf
authors.affiliation: National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA ; Boston University School of Medicine, Boston, MA, USA.
authors.forename: Kelly M
authors.initials: KM
authors.lastname: Harrington
authors.affiliation: Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Shaunna L
authors.initials: SL
authors.lastname: Clark
authors.affiliation: National Center for PTSD at VA Boston Healthcare System, Boston, MA, USA ; Boston University School of Medicine, Boston, MA, USA.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Miller
keywords: Monte Carlo simulation; bias; confirmatory factor analysis; sample size; solution propriety; statistical power; structural equation modeling