Parallel Process Latent Growth Modeling with Multivariate Confounders/Suppressors

Abstract

A parallel process growth model is often limited to the developments of two main trends due to model complexity. Two major parallel processes are only modeled to examine how the changes in two longitudinal processes might be related without considering other growth models possibly related to the two main processes. However, the exclusion of a third LGM risks introducing bias into key associations while capturing the relations between only two growth processes, since they are not controlled. This study presents a method to address the effect of spurious relationship while minimizing convergence issues when other processes are not considered. The suggested method utilizes the factor score estimates for other relevant growth models to control the spurious effect. Results revealed the superiority of the parallel process model using the factor scores over the typical parallel process model and illustrated the importance of considering growth models relevant to the parallel process model.