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Multiple-membership multiple-classification models for social network and group dependences

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  • Mark Tranmer
  • David Steel
  • William J. Browne

Abstract

type="main" xml:id="rssa12021-abs-0001"> The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications.

Suggested Citation

  • Mark Tranmer & David Steel & William J. Browne, 2014. "Multiple-membership multiple-classification models for social network and group dependences," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 439-455, February.
  • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:439-455
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Cited by:

    1. Jonathan Pratschke & Giovanni Abbiati, 2023. "“Like with like” or “do like?” Modeling peer effects in the classroom," Social Science Quarterly, Southwestern Social Science Association, vol. 104(3), pages 265-280, May.
    2. Johan Koskinen & Galina Daraganova, 2022. "Bayesian analysis of social influence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1855-1881, October.
    3. George Gerogiannis & Mark Tranmer & Duncan Lee & Thomas Valente, 2022. "A Bayesian spatio‐network model for multiple adolescent adverse health behaviours," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 271-287, March.
    4. Ciomek, Alexandra M. & Braga, Anthony A. & Papachristos, Andrew V., 2020. "The influence of firearms trafficking on gunshot injuries in a co-offending network," Social Science & Medicine, Elsevier, vol. 259(C).
    5. Philipp Meyer‐Doyle & Sunkee Lee & Constance E. Helfat, 2019. "Disentangling the microfoundations of acquisition behavior and performance," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1733-1756, November.
    6. Guanpeng Dong & Richard Harris & Kelvyn Jones & Jianhui Yu, 2015. "Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-18, June.
    7. Giovanni Abbiati & Jonathan Pratschke, 2021. "‘Like with Like’ or ‘Do Like’? Modelling Peer Effects in The Classroom," CSEF Working Papers 603, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    8. Ruth Salway & Lydia Emm-Collison & Simon J. Sebire & Janice L. Thompson & Deborah A. Lawlor & Russell Jago, 2019. "A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity," IJERPH, MDPI, vol. 16(24), pages 1-16, December.
    9. Pilar Marqués-Sánchez & María F. Muñoz-Doyague & Yolanda V. Martínez & Martin Everett & Nestor Serrano-Fuentes & Peter Van Bogaert & Ivaylo Vassilev & David Reeves, 2018. "The Importance of External Contacts in Job Performance: A Study in Healthcare Organizations Using Social Network Analysis," IJERPH, MDPI, vol. 15(7), pages 1-17, June.

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