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Area of Science:

  • Social and behavioral sciences
  • Psychology
  • Quantitative psychology

Background:

  • Multivariate time series data are valuable for studying system dynamics in social and behavioral sciences.
  • Dynamic Factor Models (DFMs) typically analyze single-unit time series, posing challenges for synchronizing data from multiple units.
  • Existing methods struggle to model intraindividual dynamics across multiple individuals simultaneously.

Purpose of the Study:

  • To present a novel Multilevel Dynamic Factor Model (MDFM) for analyzing multiple multivariate time series within multilevel Structural Equation Modeling (SEM) frameworks.
  • To disentangle within- and between-person variability while modeling intraindividual process dynamics.
  • To demonstrate the application of MDFMs using empirical data on affect in dating couples.

Main Methods:

  • Developed and applied a Multilevel Dynamic Factor Model (MDFM) within SEM.
  • Utilized lag0, lag1, and lag2 MDFMs to analyze affect data from 205 dating couples with over 50 days of observations.
  • Incorporated a model extension allowing for randomly varying dynamical coefficients.

Main Results:

  • The MDFM successfully analyzed multiple multivariate time series, disentangling individual and couple-level dynamics.
  • Empirical findings shed light on affect regulation and coregulation processes within dating couples.
  • The model extension provided insights into population-level variations in dynamical coefficients.

Conclusions:

  • MDFMs offer a powerful approach for analyzing complex, multi-unit time series data in social and behavioral research.
  • The study highlights the utility of MDFMs in understanding interpersonal dynamics, such as affect regulation in couples.
  • Future research directions include further methodological refinements and applications of MDFMs to diverse datasets.