Recovering Developmental Bivariate Trajectories in Accelerated Longitudinal Designs with Dynamic Continuous Time Modeling

Abstract

Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in psychology and related sciences imply working with several variables that are intercorrelated as they unfold over time, such as cognitive and cortical development. Therefore, bivariate developmental models are required. This study aimed to assess the effectiveness of continuous-time bivariate Latent Change Score (CT-BLCS) models for recovering the trajectories of two interdependent developmental processes using data from diverse ALDs. Through a Monte Carlo simulation study, the efficacy of different sampling designs and sample sizes was examined. The study fills a gap in the literature by examining the performance of ALDs in bivariate systems, providing specific recommendations for future application of ALDs for studying interrelated developmental variables.

Keywords:

• Accelerated longitudinal design
• bivariate developmental process
• bivariate latent change score
• continuous time modeling
• state-space models

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In some cases, dynamic errors are included in the model to consider the influence of random shocks at the latent level, although these stochastic models are not commonly used (see Cáncer et al., 2023 for further explanations of deterministic and stochastic BLCS models)

Additional information

Funding

This work was funded by the Ministry of Science and Innovation of Spain (ref. PID2019-107570GA-I00/AEI/doi: 10.13039/501100011033), granted to EE.