Growth mixture modeling with non‐normal distributions

A limiting feature of previous work on growth mixture modeling is the assumption of normally distributed variables within each latent class. With strongly non‐normal outcomes, this means that several latent classes are required to capture the observed variable distributions. Being able to relax the assumption of within‐class normality has the advantage that a non‐normal observed distribution does not necessitate using more than one class to fit...