Social Network Mediation Analysis: A Latent Space Approach

Haiyan Liu, Ick Hoon Jin, Zhiyong Zhang, Ying Yuan

Research output: Contribution to journalArticlepeer-review

Abstract

A social network comprises both actors and the social connections among them. Such connections reflect the dependence among social actors, which is essential for individuals’ mental health and social development. In this article, we propose a mediation model with a social network as a mediator to investigate the potential mediation role of a social network. In the model, the dependence among actors is accounted for by a few mutually orthogonal latent dimensions which form a social space. The individuals’ positions in such a latent social space are directly involved in the mediation process between an independent and dependent variable. After showing that all the latent dimensions are equivalent in terms of their relationship to the social network and the meaning of each dimension is arbitrary, we propose to measure the whole mediation effect of a network. Although individuals’ positions in the latent space are not unique, we rigorously articulate that the proposed network mediation effect is still well defined. We use a Bayesian estimation method to estimate the model and evaluate its performance through an extensive simulation study under representative conditions. The usefulness of the network mediation model is demonstrated through an application to a college friendship network.

Original languageEnglish
Pages (from-to)272-298
Number of pages27
JournalPsychometrika
Volume86
Issue number1
DOIs
Publication statusPublished - 2021 Mar

Bibliographical note

Funding Information:
This study was partially supported by the Institute for Scholarship in the Liberal Arts, College of Arts and Letters, University of Notre Dame, by Humanities and Social Sciences Research Project in 2020 (2020-22-0389), Yonsei University and by Basic Science Research Program through the National Research Foundation of Korea (NRF 2020R1A2C1A01009881).

Publisher Copyright:
© 2020, The Psychometric Society.

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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