Matrix Decomposition Latent Growth Model Tree

Abstract

Latent growth models (LGMs) have been widely used to describe individual trajectories of change, average change patterns, and individual differences in change within a group. Structural equation model tree (SEM Tree) partitions samples through recursive partitioning using external covariates and characterizes between-group differences via parameters of prespecified SEM template models. An SEM Tree with an LGM as its template model may therefore serve as a useful classification method for longitudinal data. However, current SEM Tree implementations may face important practical obstacles, including high computational cost and frequent improper solutions caused by model misspecification or data characteristics (e.g., a limited number of measurement occasions). In the present study, we developed a matrix decomposition LGM tree (MDLGM Tree), which applies the matrix decomposition factor analysis (MDFA) approach, an eigenvalue-decomposition-based method for model estimation. Previous studies on MDFA and its extensions to SEM have shown that this approach can reduce the risk of improper solutions while yielding estimates highly similar to those obtained by maximum likelihood estimation. Through a series of simulations, we demonstrated that, compared with conventional SEM Tree, the MDLGM Tree greatly reduces the occurrence of improper solutions and is also highly efficient in terms of computation time.