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A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures
Vieira,Fabio Leenders,Roger McFarland,Daniel Mulder,Joris
Vieira,Fabio
Leenders,Roger
McFarland,Daniel
Mulder,Joris
Abstract
Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks; while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.
Description
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
Bayesian inference, Multilevel analysis, Relational event history, Social networks
Citation
Vieira, F, Leenders, R, McFarland, D & Mulder, J 2023, 'A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures', Behaviormetrika. https://doi.org/10.1007/s41237-023-00203-4