Extending Latent Basis Growth Model to Explore Joint Development in the Framework of Individual Measurement Occasions

TL;DR

This paper extends latent basis growth models to analyze joint nonlinear developmental trajectories across multiple outcomes, accommodating individual measurement occasions and providing accurate estimates in simulations and real data.

Contribution

It introduces a multivariate extension of LBGM for interconnected trajectories with individual measurement occasions, enhancing flexibility and applicability.

Findings

Model provides unbiased, accurate estimates in simulations.

Effective in analyzing joint development in real-world data.

Includes computational code for implementation.

Abstract

Longitudinal processes often pose nonlinear change patterns. Latent basis growth models (LBGMs) provide a versatile solution without requiring specific functional forms. Building on the LBGM specification for unequally-spaced waves and individual occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. This provides a unified approach to nonlinear, interconnected trajectories. Simulation studies demonstrate that the proposed model can provide unbiased and accurate estimates with target coverage probabilities for the parameters of interest. Real-world analyses of reading and mathematics scores demonstrates its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.