Dissertation/Thesis Abstract

The author has requested that access to this graduate work be delayed until 2020-02-04. After this date, this graduate work will be available on an open access basis.
Social Network Analysis in an Extended Structural Equation Modeling Framework
by Liu, Haiyan, Ph.D., University of Notre Dame, 2018, 168; 13836262
Abstract (Summary)

A primary focus of social network analysis (SNA) is to understand actor attributes from social structures in a network. It is an interdisciplinary research topic of statistics, sociology, graph theories, and computer sciences. Despite its popularity in other fields, SNA is under-utilized in psychological and educational research. This is largely due to the lack of easy-to-use models and user-friendly software. To fill the gap, this dissertation proposes three models for SNA under an extended structural equation modeling (SEM) framework. The first model is a latent space model with a factor structure. In this model, a social network is the outcome variable and the model intends to identify covariates predicting a network. As a generalization of the first model, the second model focuses on social networks with ordinal relations among actors. A Probit regression model is used to study the association of an ordinal social network and covariates. Both models are estimated using a two-stage maximum likelihood (ML) method. The performance of the two-stage ML method is assessed through Monte Carlo simulation studies. Simulation results show that the two-stage ML method can recover both model parameters and standard errors. The third model is a mediation model with a social network as a mediator. In this model, a latent space model is used to extract underlying factors of a social network, which directly participate in the causal process between two variables. To estimate the model, a Bayesian estimation method is used and its performance is evaluated through a simulation study. The usefulness of three models is demonstrated in analyzing a friendship network data set.

Indexing (document details)
Advisor: Zhang, Zhiyong
Commitee: Jin, Ick Hoon, Wang, Lijuan, Yuan, Ke-Hai, Zhang, Zhiyong
School: University of Notre Dame
Department: Psychology
School Location: United States -- Indiana
Source: DAI-B 80/06(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Bayesian analysis, Logistic model, Mediation analysis, Personality, Social network analysis, Structural equation modeling
Publication Number: 13836262
ISBN: 978-0-438-83395-1
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy