Measurement Invariance for Second-Order Latent Growth Models: Frequentist and Bayesian Approaches

Measurement invariance over time is an important assumption of latent growth modeling. Traditional methods for modeling invariance have been criticized. This study investigated Bayesian approximate invariance as a viable alternative to these traditional methods. Results indicate that Bayesian approximate invariance may be beneficial for modeling invariance of categorical response items.