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Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models

Published online by Cambridge University Press:  01 January 2025

Teague R. Henry Open the ORCID record for Teague R. Henry [Opens in a new window] ,
Kathleen M. Gates ,
Mitchell J. Prinstein  and
Douglas Steinley
Teague R. Henry*
Affiliation:
University of North Carolina at Chapel Hill
Kathleen M. Gates
Affiliation:
University of North Carolina at Chapel Hill
Mitchell J. Prinstein
Affiliation:
University of North Carolina at Chapel Hill
Douglas Steinley
Affiliation:
University of Missouri
*
Correspondence should be made to Teague R. Henry, University of North Carolina at Chapel Hill, Chapel Hill, USA. Email: trhenry@email.unc.edu
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Abstract

This article develops a class of models called sender/receiver finite mixture exponential random graph models (SRFM-ERGMs). This class of models extends the existing exponential random graph modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features without a block structure. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that unobserved heterogeneity in effects of nodal covariates can be a major cause of misfit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.

Keywords

p*exponential random graphsfinite mixture modelingindividual differences modeling
Type
Original Research
Information
Psychometrika , Volume 85 , Issue 1 , March 2020 , pp. 8 - 34
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-019-09685-2) contains supplementary material, which is available to authorized users.

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