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

Teague R. Henry (), Kathleen M. Gates, Mitchell J. Prinstein and Douglas Steinley
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Teague R. Henry: University of North Carolina at Chapel Hill
Kathleen M. Gates: University of North Carolina at Chapel Hill
Mitchell J. Prinstein: University of North Carolina at Chapel Hill
Douglas Steinley: University of Missouri

Psychometrika, 2020, vol. 85, issue 1, No 3, 8-34

Abstract: 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 graphs; finite mixture modeling; individual differences modeling (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11336-019-09685-2

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