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Social Network Mediation Analysis: A Latent Space Approach

Published online by Cambridge University Press:  01 January 2025

Haiyan Liu Open the ORCID record for Haiyan Liu [Opens in a new window] ,
Ick Hoon Jin ,
Zhiyong Zhang  and
Ying Yuan
Haiyan Liu*
Affiliation:
University of California, Merced
Ick Hoon Jin
Affiliation:
Yonsei University
Zhiyong Zhang
Affiliation:
University of Notre Dame
Ying Yuan
Affiliation:
The University of Texas MD, Anderson Cancer Center
*
Correspondence should be made to Haiyan Liu, Psychological Sciences, University of California, Merced, 5200 N. Lake Road, Merced, CA 95343, USA. Email: [email protected]
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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.

Keywords

friendship networkmediation analysissocial network analysislatent space modelingBayesian estimationsmoking behavior
Type
Theory and Methods (T&M)
Information
Psychometrika , Volume 86 , Issue 1 , March 2021 , pp. 272 - 298
Copyright
Copyright © 2020 The Psychometric Society

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