Literature DB >> 28463022

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis.

Danielle O Dean1, Daniel J Bauer2, Mitchell J Prinstein3.   

Abstract

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed.

Entities:  

Keywords:  Event history analysis; generalized linear modeling; multilevel modeling; social network analysis; survival analysis

Mesh:

Year:  2017        PMID: 28463022      PMCID: PMC5985444          DOI: 10.1080/00273171.2016.1267605

Source DB:  PubMed          Journal:  Multivariate Behav Res        ISSN: 0027-3171            Impact factor:   5.923


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Authors:  Amy C Hartl; Brett Laursen; Antonius H N Cillessen
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Journal:  Soc Sci Res       Date:  2012-04-01

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Authors:  Mette Villingshøj; Lone Ross; Birthe Lykke Thomsen; Christoffer Johansen
Journal:  Eur J Cancer       Date:  2006-09-27       Impact factor: 9.162

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Authors:  David R Schaefer; John M Light; Richard A Fabes; Laura D Hanish; Carol Lynn Martin
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