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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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    1. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    4. Michael Schweinberger & Mark S. Handcock, 2015. "Local dependence in random graph models: characterization, properties and statistical inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 647-676, June.
    5. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    6. Nowicki K. & Snijders T. A. B., 2001. "Estimation and Prediction for Stochastic Blockstructures," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1077-1087, September.
    7. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    8. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    9. Pavel N. Krivitsky & Mark S. Handcock, 2014. "A separable model for dynamic networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 29-46, January.
    10. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    11. Peter Bryant, 1991. "Large-sample results for optimization-based clustering methods," Journal of Classification, Springer;The Classification Society, vol. 8(1), pages 31-44, January.
    12. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    13. Govaert, G. & Nadif, M., 1996. "Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 65-81, November.
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