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A tool to simulate and visualize dyadic interaction dynamics.

Sophie W Berkhout1, Noémi K Schuurman1, Ellen L Hamaker1

  • 1Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University.

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Summary
This summary is machine-generated.

This study introduces the Dyadic Interaction Dynamics (DID) Shiny app for simulating and visualizing dyadic interaction data. The app aids researchers in selecting appropriate dynamic modeling approaches for their research questions and data.

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Area of Science:

  • Social Psychology
  • Quantitative Psychology
  • Computational Social Science

Background:

  • Dynamic models are increasingly utilized for analyzing dyadic interactions.
  • Existing models for dyadic data analysis require accessible tools for simulation and visualization.

Purpose of the Study:

  • To present the Dyadic Interaction Dynamics (DID) Shiny app for simulating and visualizing dyadic interaction data.
  • To facilitate theory development by using data generation as a guiding tool.
  • To help researchers choose the most suitable dynamic modeling approach for their specific research questions and data.

Main Methods:

  • The study discusses the fundamentals of dynamic models in the context of dyadic interactions.
  • It presents six distinct dynamic models: VAR(1), latent VAR(1), time-varying VAR(1), threshold VAR(1), hidden Markov model, and Markov-switching VAR(1).
  • The Dyadic Interaction Dynamics (DID) Shiny app is introduced for generating and visualizing data from these models.

Main Results:

  • The DID Shiny app allows for the simulation and visualization of data generated by various dyadic interaction models.
  • Illustrations of model-implied behavior are provided through generated data examples.
  • Empirical examples are used to demonstrate the application of the presented models.

Conclusions:

  • The DID Shiny app serves as a valuable resource for researchers studying dyadic interactions.
  • The app enhances understanding of different dynamic modeling approaches and their applicability.
  • Data generation via the app can guide theoretical development in the field of dyadic interaction research.