Fitting latent growth models with small sample sizes and non-normal missing data

This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML estimators, MLR was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates and growth parameter coverage. However, the choice MLMV produced the most accurate p values for the chi(2) test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (i.e., comparative fit index, root mean square error of approximation, and standardized root mean square residual) exhibited worse fit. When the sample size was very small (e.g., N < 60), the fit indices would imply that a proposed model fit poorly, when this might not be actually the case in the population.

Automated summary

The study found that robust maximum likelihood (ML) estimators can handle non-normal missing data in small samples, with MLR being the optimal choice in such cases. As sample size decreased, fit indices exhibited worse fit, cautioning against overinterpretation of model fit.