Jenss-Bayley Latent Change Score Model with Individual Ratio of Growth Acceleration in the Framework of Individual Measurement Occasions

TL;DR

This paper introduces an extended latent change score model based on Jenss-Bayley growth curves, capable of handling unequally spaced measurement occasions and estimating individual growth acceleration ratios, validated through simulations and real data.

Contribution

The paper develops a novel extension of the LCSM with Jenss-Bayley curves, allowing for flexible measurement timings and individual growth acceleration estimation, which improves accuracy over existing models.

Findings

Model estimates parameters unbiasedly and precisely.

Proposed model outperforms existing models in simulations.

Successfully applied to real-world reading score data.

Abstract

Longitudinal data analysis has been widely employed to examine between-individual differences in within-individual changes. One challenge of such analyses is that the rate-of-change is only available indirectly when change patterns are nonlinear with respect to time. Latent change score models (LCSMs), which can be employed to investigate the change in rate-of-change at the individual level, have been developed to address this challenge. We extend an existing LCSM with the Jenss-Bayley growth curve \cite[Chapter~18]{Grimm2016growth} and propose a novel expression for change scores that allows for (1) unequally-spaced study waves and (2) individual measurement occasions around each wave. We also extend the existing model to estimate the individual ratio of the growth acceleration (that largely determines the trajectory shape and is viewed as the most important parameter in the Jenss-Bayley model). We present the proposed model by a simulation study and a real-world data analysis. Our simulation study demonstrates that the proposed model can estimate the parameters unbiasedly and precisely and exhibit target confidence interval coverage. The simulation study also shows that the proposed model with the novel expression for the change scores outperforms the existing model. An empirical example using longitudinal reading scores shows that the model can estimate the individual ratio of the growth acceleration and generate individual rate-of-change in practice. We also provide the corresponding code for the proposed model.