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Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation

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  • Tracy M. Sweet

    (Department of Human Development and Quantitative Methodology, University of Maryland)

Abstract

There are some educational interventions aimed at changing the ways in which individuals interact, and social networks are particularly useful for quantifying these changes. For many of these interventions, the ultimate goal is to change some outcome of interest such as teacher quality or student achievement, and social networks act as a natural mediator; the intervention changes the social networks of the teachers in schools, and teachers with certain types of social networks tend to use better teaching practices, for example. Due to lack of methodology, however, social networks have not been modeled as mediators. We present a new framework for modeling social networks as mediators in which a social network model is embedded into a mediation model and both models are estimated simultaneously. As a proof of concept, we introduce a new network model for mediation, applicable for interventions that affect subgroup structure. We provide a small simulation study to demonstrate the feasibility of this model and explore some potential operating characteristics. Finally, we apply our model to examine the effects of instructional coaches on teacher advice-seeking networks and subsequent changes in beliefs about mathematics.

Suggested Citation

  • Tracy M. Sweet, 2019. "Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 210-240, April.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:2:p:210-240
    DOI: 10.3102/1076998618814255
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    References listed on IDEAS

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    1. Imai, Kosuke & Keele, Luke & Tingley, Dustin & Yamamoto, Teppei, 2011. "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies," American Political Science Review, Cambridge University Press, vol. 105(4), pages 765-789, November.
    2. 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.
    3. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    4. 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.
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    Cited by:

    1. Tracy Sweet & Samrachana Adhikari, 2020. "A Latent Space Network Model for Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 251-274, June.
    2. Haiyan Liu & Ick Hoon Jin & Zhiyong Zhang & Ying Yuan, 2021. "Social Network Mediation Analysis: A Latent Space Approach," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 272-298, March.
    3. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2022. "Networks as mediating variables: a Bayesian latent space approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1015-1035, October.

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