Utilizing multilevel event history analysis to model temporal characteristics of friendships unfolding in discrete-time social networks Public Deposited

Downloadable Content

Download PDF
Last Modified
  • March 19, 2019
Creator
  • Dean, Danielle
    • Affiliation: College of Arts and Sciences, Department of Psychology and Neuroscience
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. Adding to these methods, a modeling framework utilizing discrete-time multilevel survival analysis is proposed in this dissertation to answer questions about temporal characteristics of friendships, such as the processes leading to friendship dissolution or how long it takes an individual to reciprocate a friendship. While the modeling framework is introduced in terms of understanding friendships, it can be used to understand micro-level dynamics of a social network more generally, such as the duration of reciprocated ties (or undirected relations) and the timing of reciprocal actions. Similar to the model proposed by de Nooy (2011), these models can be fit with standard generalized linear mixed model software, after transforming the data from a network representation to a pair-period dataset. Two main models are introduced as part of the framework, and a simulation study and empirical example are proposed for each. The first empirical example concerns friendship duration in high school students and the second concerns the timing of reciprocal "following" actions on the social network site Twitter. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed.
Date of publication
Keyword
Subject
DOI
Identifier
Resource type
Rights statement
  • In Copyright
Advisor
  • Bauer, Daniel
  • Thissen, David
  • Zeng, Donglin
  • Gates, Kathleen
  • Prinstein, Mitchell J.
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2015
Language
Publisher
Place of publication
  • Chapel Hill, NC
Access
  • There are no restrictions to this item.
Parents:

This work has no parents.

Items