Network Mediation Analysis Using Model-Based Eigenvalue Decomposition. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Che, Chang; Jin, Ick Hoon; Zhang, Zhiyong
• Is Part Of: Structural equation modeling. Volume 28:Issue 1(2021)
• Page Start: 148
• Page End: 161
• Publication Date: 2021-01-02
• Subjects: Network analysis -- mediation analysis -- model-based eigenvalue decomposition -- latent variables; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1721292
• Languages: English
• ISSNs: 1070-5511

Abstract

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.