The Impact of Priors on Bayesian Nonlinear Growth Models

Longitudinal researchers need flexible models for nonlinear within-person change. We compare two candidate nonlinear models, the Gompertz curve and P-Spline, under various Bayesian prior conditions. We examine results from different prior specifications and show that the Bayesian framework makes P-Splines a highly adaptable method for capturing nonlinear trajectories.