🤖 AI Summary
This study addresses the challenge of modeling multivariate longitudinal data characterized by individually varying measurement occasions, nonlinear developmental trajectories, and interdependent outcomes. We propose the Multivariate Latent Baseline Growth Model (MLBGM), a structural equation modeling–based framework that extends the latent baseline growth concept to joint modeling of multiple outcomes. MLBGM explicitly incorporates person-specific measurement times, thereby relaxing the restrictive assumption of equidistant assessments inherent in conventional growth models. Monte Carlo simulations demonstrate unbiased parameter estimates and adequate confidence interval coverage. Applied to large-scale educational longitudinal data, MLBGM uncovers asynchronous, nonlinear, and coupled developmental patterns between literacy and mathematics competencies. Our key contributions are: (1) the first latent growth model accommodating both individually varying measurement times and multidimensional nonlinear co-evolution; and (2) publicly available open-source implementation code.
📝 Abstract
Longitudinal processes often exhibit 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 measurement occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. The extended models enable the analysis of nonlinear parallel longitudinal processes with unequally-spaced study waves in the framework of individual measurement occasions. We present the proposed models by simulation studies and real-world data analyses. 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 demonstrate its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.