A tutorial for modeling the evolution of network dynamics for multiple groups

Andrew Pilny, Luisa Ruge-Jones, Marshall Scott Poole

Research output: Contribution to journalArticlepeer-review


Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.

Original languageEnglish
Article number982066
JournalFrontiers in Human Dynamics
StatePublished - 2022

Bibliographical note

Publisher Copyright:
Copyright © 2023 Pilny, Ruge-Jones and Poole.


  • Bayesian estimation
  • evolution
  • multi-level model
  • networks
  • social network analysis
  • teams

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Management, Monitoring, Policy and Law
  • Sociology and Political Science
  • Demography


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