Proportionality Assumption in Latent Basis Curve Models: A Cautionary Note. Issue 1 (2nd January 2016) - Wu, Wei; Lang, Kyle M.
Abstract : Latent basis curve models (LBCMs) have been popular in modeling change when the change trajectories are unknown or nonlinear. The estimated change trajectories from LBCMs are often viewed as optimal and used as reference points against which other change trajectories are tested. However, there is a proportionality assumption underlying LBCMs that has received little attention from researchers. This study uses a Monte Carlo simulation to show that violation of this assumption can potentially result in substantially biased estimates of the means and variances of changes and covariate effects on these changes, leading to incorrect statistical inference. The implications of the simulation study are discussed and alternatives to LBCMs are suggested for use when the proportionality assumption is likely to be violated.