Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model

Abstract

The piecewise growth mixture model is used in longitudinal studies to tackle non-continuous trajectories and unobserved heterogeneity in a compound way. This study investigated how factors such as latent distance and shape influence the model. Two simulation studies were used exploring the 2- and 3-class situation with sample size, latent distance (Mahalanobis distance), and shape being considered as the influencing factor. The results of two simulations showed that a non-parallel shape led to a slightly better overall model fit. Parameter estimation is affected by the shape, mainly through the parameter differences between latent classes.