Structural Equation Modeling of Social Networks: Specification, Estimation, and Application

Haiyan Liu, Ick Hoon Jin, Zhiyong Zhang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.

Original languageEnglish
Pages (from-to)714-730
Number of pages17
JournalMultivariate Behavioral Research
Volume53
Issue number5
DOIs
Publication statusPublished - 2018 Sep 3

Bibliographical note

Funding Information:
Funding: This work was supported by Grant R305D140037 from the Institute of Education Sciences and Grant 1461355 from National Science Foundation. This work was also partially supported by the Institute for Scholarship in the Liberal Arts, College of Arts and Letters, University of Notre Dame.

Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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