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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Teague R. Henry & Kathleen M. Gates & Mitchell J. Prinstein & Douglas Steinley, 2020. "Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 8-34, March.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:1:d:10.1007_s11336-019-09685-2
    DOI: 10.1007/s11336-019-09685-2
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    References listed on IDEAS

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