Abstract
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 language | English |
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Article number | 982066 |
Journal | Frontiers in Human Dynamics |
Volume | 4 |
DOIs | |
State | Published - 2022 |
Bibliographical note
Publisher Copyright:Copyright © 2023 Pilny, Ruge-Jones and Poole.
Keywords
- 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