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Summary
This summary is machine-generated.

Bayesian multilevel vector autoregressive (mlVAR) models offer powerful tools for analyzing how individuals change over time. This study compares Bayesian software options (Stan, JAGS, Mplus) for fitting complex mlVAR models, aiding social science researchers.

Keywords:
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Area of Science:

  • Psychology
  • Statistics
  • Computational Social Science

Background:

  • Intensive longitudinal data necessitates advanced statistical models for understanding individual change.
  • Multilevel vector autoregressive (mlVAR) models are increasingly recognized for analyzing dynamic processes and individual differences.
  • Frequentist approaches struggle with complex mlVAR models, whereas Bayesian methods offer computational advantages.

Purpose of the Study:

  • To provide accessible, step-by-step guidance on fitting Bayesian mlVAR models.
  • To compare the usability and performance of Stan, JAGS, and Mplus for Bayesian mlVAR analysis.
  • To demonstrate the application of Bayesian mlVAR models in analyzing affective dynamics.

Main Methods:

  • Step-by-step illustrations for fitting Bayesian mlVAR models using Stan, JAGS, and Mplus.
  • A Monte Carlo simulation study to evaluate model performance.
  • Application of mlVAR models to an empirical dataset on affective dynamics.

Main Results:

  • Bayesian frameworks, particularly with MCMC techniques, effectively handle complex and high-dimensional mlVAR models.
  • Stan, JAGS, and Mplus provide viable options for fitting Bayesian mlVAR models, with specific strengths and weaknesses.
  • The empirical example highlights the utility of mlVAR for capturing intra- and inter-individual variations in dynamic processes.

Conclusions:

  • Bayesian mlVAR modeling is a powerful and accessible approach for analyzing complex longitudinal data in social sciences.
  • Familiarity with Bayesian software like Stan, JAGS, and Mplus can enhance researchers' ability to model individual change.
  • This work facilitates the adoption of advanced statistical techniques for studying dynamic psychological processes.