The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation

Latent basis growth modeling is a flexible version of the growth curve modeling, in which it allows the basis coefficients of the model to be freely estimated, and thus the optimal growth trajectories can be determined from the observed data. In this article, Bayesian estimation methods are applied for latent basis growth modeling. Because the latent basis coefficients are important parameters that determine the growth pattern in latent basis growth models, we evaluate the impact of different priors for the basis coefficients on parameter recovery and model estimation. Noninformative priors, informative priors with varying levels of accuracy and precision, and data-dependent priors are considered. In addition, the issue of model specification is treated as a prior selection procedure. The impact of model misspecification and priors for model parameters are investigated simultaneously. A Monte Carlo simulation study is conducted and suggests that misspecified models adversely affect the parameter estimation much more than inaccurate priors. Recommendations on prior selection in latent basis growth models are given based on the simulation results. A real data example on the development of schoolchildren’s reading ability is also provided to illustrate the comparison among different sets of priors.