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Bayesian Analysis of Multilevel Probit Models for Data With Friendship Dependencies

Author

Listed:
  • Johan Koskinen

    (University of Manchester)

  • Sten-Ã…ke Stenberg

    (The Swedish Institute for Social Research (SOFI))

Abstract

When studying educational aspirations of adolescents, it is unrealistic to assume that the aspirations of pupils are independent of those of their friends. Considerable attention has also been given to the study of peer influence in the educational and behavioral literature. Typically, in empirical studies, the friendship networks have either been reduced to some summary measure or, when the complexity of the social structure has been used to its full extent, the studies have been limited to single networks, thereby effectively neglecting the context of the studied network. The authors propose a multilevel probit model with correlated responses and a Markov chain Monte Carlo–based inference scheme for analyzing multilevel data where there may be dependencies not only within levels but also between peers that are directly relationally tied. The authors describe the procedures in the context of the illustrative case of the Stockholm Birth Cohort, and the decision to proceed to higher secondary education for 12,660 pupils in 619 school classes in Stockholm.

Suggested Citation

  • Johan Koskinen & Sten-Ã…ke Stenberg, 2012. "Bayesian Analysis of Multilevel Probit Models for Data With Friendship Dependencies," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 203-230, April.
    • Handle: RePEc:sae:jedbes:v:37:y:2012:i:2:p:203-230
    DOI: 10.3102/1076998611402504
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    References listed on IDEAS

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    1. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
    2. D. Clayton & J. Rasbash, 1999. "Estimation in large cross random‐effect models by data augmentation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 425-436.
    3. Garry Robins & Philippa Pattison & Peter Elliott, 2001. "Network models for social influence processes," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 161-189, June.
    4. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    5. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
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