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…

The objective of this study is to examine the trajectory of internalizing problems across middle childhood among a population sample of Australian children, and to understand the timing of explanatory factors related to children's development of internalizing problems, by using multiple-indicator latent growth curve modeling. Participants were…