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Modeling within-level latent interaction effects in multilevel vector-autoregressive models.

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Summary
This summary is machine-generated.

This study introduces advanced multilevel latent time-series models to capture how within-person dynamics change over time, accounting for time-varying moderators. These models offer a more nuanced understanding of complex psychological processes.

Keywords:
Dynamic structural equation modelingIntensive longitudinal dataLatent interactionModerationTime series analysis

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Area of Science:

  • Psychological Science
  • Quantitative Psychology
  • Longitudinal Data Analysis

Background:

  • Multilevel (latent) time-series models are increasingly used for within-person dynamics.
  • Current models often overlook time-varying moderators influencing longitudinal relationships.
  • This limits understanding of how dynamic within-person processes are affected by changing factors.

Purpose of the Study:

  • To extend multilevel latent time-series models by incorporating latent interaction effects at the within-person level.
  • To provide a tutorial for applying these enhanced models using Bayesian estimation.
  • To investigate the temporal dynamics of negative affect, rumination, and mindful attention.

Main Methods:

  • Extension of multilevel latent time-series models to include latent interaction effects.
  • Bayesian estimation via Markov chain Monte Carlo (MCMC) techniques.
  • Simulation studies to evaluate model performance and complexity.

Main Results:

  • Demonstrated successful incorporation of latent interaction effects in dynamic within-person analyses.
  • Provided empirical examples using negative affect, rumination, and mindful attention.
  • Offered recommendations on model complexity and sample size requirements based on simulations.

Conclusions:

  • The enhanced models provide a more comprehensive approach to studying time-dependent within-person dynamics.
  • Applied researchers can utilize these models to explore nuanced longitudinal relationships.
  • Adequate sample sizes (e.g., 100 time points for 100 persons) are crucial for complex random-effects models.