On the Requirements of Non-linear Dynamic Latent Class SEM: A Simulation Study with Varying Numbers of Subjects and Time Points

Abstract

Although small sample sizes represent an important issue, few studies investigated the requirements in dynamic latent variable model frameworks (e.g., dynamic structural equation modeling, DSEM; dynamic latent class analysis, DLCA). We conduct a small sample performance study of Bayesian estimation for the non-linear dynamic latent class structural equation model which generalizes DSEM and DLCA to include time-dependent latent class transitions. We simulate data using a two-level (non-linear) dynamic latent class model with a varying number of subjects () and time points (T = 10, 25, 50) which are in our main focus among other simulation conditions. The results show that at least a sample size of with is required to ensure good estimates. Using diffuse priors on the between level, especially for the (co-)variance parameters and the factor loadings should be avoided.