A hierarchical latent space network model for mediation. (30th June 2022)

• Record Type: Journal Article
• Authors: Sweet, Tracy M.; Adhikari, Samrachana
• Is Part Of: Network science. Volume 10:Number 2(2022)
• Page Start: 113
• Page End: 130
• Publication Date: 2022-06-30
• Subjects: social network models -- mediation analysis -- Bayesian; Social networks -- Research -- Periodicals; System analysis -- Periodicals; System theory -- Periodicals; Computer science -- Periodicals; 003.72
• DOI: 10.1017/nws.2022.12
• Languages: English
• ISSNs: 2050-1242

Abstract

Abstract: For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019 ) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.