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Monte Carlo methods were used to systematically study the effects of sampling error and model characteristics upon parameter estimates and their associated standard errors in maximum likelihood confirmatory factor analysis. Sample sizes were varied from 50 to 300 for models defined by different numbers of indicators per factor, numbers of factors, correlations between factors, and indicator reliabilities. The measurement and structural parameter estimates were generally unbiased except for the structural parameters relating factors defined by only two indicators. Sampling variability can be quite large, though, particularly as sample size becomes smaller, there are fewer indicators per factor and the reliabilities are lower. However, the estimated standard errors were adjusted accordingly.
Gerbing et al. (Mon,) studied this question.