Network Mediation Analysis Using Model-Based Eigenvalue Decomposition

Chang Che, Ick Hoon Jin, Zhiyong Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a new two-stage network mediation method based on the use of a latent network approach–model-based eigenvalue decomposition–for analyzing social network data with nodal covariates. In the decomposition stage of the observed network, no assumption on the metric of the latent space structure is required. In the mediation stage, the most important eigenvectors of a network are used as mediators. This method further offers an innovative way for controlling for the conditional covariates, and it only considers the information left in the network. We demonstrate this approach in a detailed tutorial R code provided for four separate cases–unconditional and conditional model-based eigenvalue decompositions for either a continuous outcome or a binary outcome–to show its applicability to empirical network data.

Original languageEnglish
Pages (from-to)148-161
Number of pages14
JournalStructural Equation Modeling
Volume28
Issue number1
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by a private unit at University of Notre Dame.

Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Modelling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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