Sample Size and Number of Parameter Estimates in Maximum Likelihood Confirmatory Factor Analysis: A Monte Carlo Investigation

A number of authors have proposed that determining an adequate sample size in structural equation modeling can be aided by considering the number of parameters to be estimated. This study directly investigates this assumption in the context of maximum likelihood confirmatory factor analysis. The findings support previous research on the effect of sample size, measured-variable reliability, and the number of measured variables per factor. However, no practically significant effect was found for the number of observations per estimated parameter.