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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 ofwithis 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.

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