Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data.

ABSTRACT: Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates to a degree. This study thus used Monte Carlo simulation methods to examin

SUBMITTER: Clark DA 

PROVIDER: S-EPMC8986125 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature