Dynamical Properties and Conceptual Interpretation of Latent Change Score Models

Abstract

Latent Change Score models (LCS) are a popular tool for the study of dynamics in longitudinal research. They represent processes in which the short-term dynamics have direct and indirect consequences on the long-term behavior of the system. However, this dual interpretation of the model parameters is usually overlooked in the literature, and researchers often find it difficult to see the connection between parameters and specific patterns of change. The goal of this paper is to provide a comprehensive examination of the meaning and interpretation of the parameters in LCS models. Importantly, we focus on their relation to the shape of the trajectories and explain how different specifications of the LCS model involve particular assumptions about the mechanisms of change. On a supplementary website, we present an interactive Shiny App that allows users to explore different sets of parameter values and examine their effects on the predicted trajectories. We also include fully explained code to estimate some of the most relevant specifications of the LCS model with the R-packages lavaan and OpenMx.

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

“…Larger variances for the additive components indicate larger inter-individual differences in the asymptotes or maximum levels of the trajectories. For a detailed account of the relations between LCS model parameters and their interpretation, see Cáncer et al (2021).…”

lcsm: An R package and tutorial on latent change score modelling

“…Multiple script-based software packages support structural equation modelling and can be used for analysing LCSMs, for example lavaan (Rosseel, 2012), OpenMx (Neale et al, 2016), or Mplus (Muthén & Muthén, 2017 Although there exist many tutorials on how to implement latent change score models in different software packages (e.g., Cáncer, Estrada, Ollero, & Ferrer, 2021;Ghisletta & McArdle, 2012;Grimm, Ram, & Estabrook, 2017;Kievit et al, 2018;Klopack & Wickrama, 2020), to the best of our knowledge, there is currently no tool that automatically generates syntax for LCSMs with different model specifications.…”

lcsm: An R package and tutorial on latent change score modelling

“…The R package RAMpath 21 offers a framework for analysing longitudinal structural equation models and it can also estimate basic univariate and bivariate LCSMs. Although there exist many tutorials on how to implement latent change score models in different software packages [e.g., 4 , 10 , 22 – 24 ], most require the user to generate their own syntax from scratch, or manually adapt existing examples to match their data and model specifications. To our knowledge, there is currently no tool that allows the user to input different model specifications and automatically generate the corresponding LCSM syntax.…”