Effectiveness of the Deterministic and Stochastic Bivariate Latent Change Score Models for Longitudinal Research

Abstract

The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the latent trajectories are affected by stochastic innovations (i.e., dynamic error). The deterministic specification of the BLCS model does not account for the effect of these innovations in the system. In contrast, the stochastic specification of the BLCS model includes parameters that capture the effect of such innovations at the latent level. Through Monte Carlo simulation, we generated two developmental processes and examined the recovery of the parameters in the deterministic and stochastic BLCS models under a broad range of empirically relevant conditions. Based on our findings, we provide specific guidelines and recommendations for the application of BLCS models in developmental research.

Keywords:

• Latent change score model
• longitudinal data analysis
• stochastic dynamical systems
• stochastic innovations
• structural equation models

Disclosure Statement

No potential conflict of interest was reported by the author(s). The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institutions or funding agencies is not intended and should not be inferred.

Notes

1 We did not use the relative bias because, when the trajectories are not affected by innovations, the dynamic error variances and covariances equal zero. Thus, computing the relative bias in these conditions would imply dividing by zero.

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].