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Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators.
- Source :
-
Multivariate Behavioral Research . Sep/Oct2024, Vol. 59 Issue 5, p934-956. 23p. - Publication Year :
- 2024
-
Abstract
- Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00273171
- Volume :
- 59
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Multivariate Behavioral Research
- Publication Type :
- Academic Journal
- Accession number :
- 179941510
- Full Text :
- https://doi.org/10.1080/00273171.2024.2347959