Abstract
Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another—in short, changing the professional social networks of educators. Current methods of social network analysis are ill-suited to modeling the multiple partially exchangeable networks that arise in randomized field trials and observational studies in which multiple classrooms, schools, or districts are involved, and to detecting the effect of an intervention on the social network itself. To address these needs, we introduce a new modeling framework, the Hierarchical Network Models (HNM) framework. The HNM framework can be used to extend single-network statistical network models to multiple networks, using a hierarchical modeling approach. We show how to generalize the latent space model for a single network to the HNM/multiple-network setting, and illustrate our approach with real and simulated social network data among education professionals.
References
|
Airoldi, E., Blei, D., Fienberg, S., Xing, E. (2008). Mixed membership stochastic block-models. The Journal of Machine Learning Research, 9, 1981–2014. Google Scholar | Medline | |
|
Bonsignore, E., Hansen, D., Galyardt, A., Aleahmad, T., Hargadon, S. (2011). The power of social networking for professional development. In Gray, T., Silver-Pacuilla, H. (Eds.), Breakthrough teaching and learning: How educational and assistive technologies are driving innovation (pp. 25–52). New York, NY: Springer. Google Scholar | Crossref | |
|
Coburn, C., Russell, J. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30, 203–235. Google Scholar | SAGE Journals | |
|
Daly, A., Moolenaar, N., Bolivar, J., Burke, P. (2010). Relationships in reform: The role of teachers’ social networks. Journal of Educational Administration, 48, 359–391. Google Scholar | Crossref | |
|
Fienberg, S., Meyer, M., Wasserman, S. (1985). Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, 51–67. Google Scholar | Crossref | |
|
Frank, K. A., Penuel, W. R., Sun, M., Chong, M. K., Singleton, C. (2013). The organization as a filter of institutional diffusion. Teachers College Record, 115(1). Google Scholar | |
|
Frank, K. A., Zhao, Y., Borman, K. (2004). Social capital and the diffusion of innovations within organizations: The case of computer technology in schools. Sociology of Education, 77, 148–171. Google Scholar | SAGE Journals | |
|
Gelman, A., Carlin, J., Stern, H., Rubin, D. (2004). Bayesian data analysis. Boca Raton, FL: CRC press. Google Scholar | |
|
Glennan, T. K., Resnick, L. B. (2004). Expanding the reach of education reforms perspectives from leaders in the scale-up of educational interventions. In Glennan, T. K., Bodill, S. J., Galegher, J. R., Kerr, K. A. (Eds.), School districts as learning organizations: A strategy for scaling education reform (Chap. 14, pp. 517–563). Santa Monica, CA: RAND Corporation. Google Scholar | |
|
Goldenberg, A., Zheng, A., Fienberg, S., Airoldi, E. (2009). A survey of statistical network models. Foundations and Trends in Machine Learning, 2, 129–133. Google Scholar | Crossref | |
|
Harris, K., Halpern, C., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., Udry, J. (2009). The national longitudinal study of adolescent health: Research design. Retrieved from http://www.cpc.unc.edu/projects/addhealth/design Google Scholar | |
|
Hoff, P. D., Raftery, A. E., Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098. Google Scholar | Crossref | |
|
Hord, S., Roussin, J., Sommers, W. (2010). Guiding professional learning communities: Inspiration, challenge, surprise and meaning. Thousand Oaks, CA: Corwin/Sage. Google Scholar | |
|
Johnson, M., Junker, B. (2003). Using data augmentation and Markov chain Monte Carlo for the estimation of unfolding response models. Journal of Educational and Behavioral Statistics, 28, 195. Google Scholar | SAGE Journals | |
|
Kolaczyk, E. (2009). Statistical analysis of network data: Methods and models. New York, NY: Springer-Verlag. Google Scholar | Crossref | |
|
Lazega, E., van Duijn, M. (1997). Position in formal structure, personal characteristics and choices of advisors in a law firm: A logistic regression model for dyadic network data. Social Networks, 19, 375–397. Google Scholar | Crossref | |
|
Lin, N. (1999). Building a network theory of social capital. Connections, 22, 28–51. Google Scholar | |
|
Matsumura, L., Garnier, H., Resnick, L. (2010). Implementing literacy coaching: The role of school social resources. Educational Evaluation and Policy Analysis, 32, 249–272. Google Scholar | SAGE Journals | |
|
McLaughlin, M., Talbert, J. (2006). Building school-based teacher learning communities: Professional strategies to improve student achievement (Vol. 45). New York, NY: Teachers College Press. Google Scholar | |
|
Moreno, J. (1934). Who shall survive?: A new approach to the problem of human interrelations. Washington, DC: Nervous and Mental Disease. Google Scholar | Crossref | |
|
Moolenaar, N., Daly, A., Sleegers, P. (2010). Occupying the principal position: Examining relationships between transformational leadership, social network position, and schools’ innovative climate. Educational Administration Quarterly, 46, 623. Google Scholar | SAGE Journals | |
|
Penuel, W., Riel, M., Joshi, A., Pearlman, L., Kim, C., Frank, K. (2010). The alignment of the informal and formal organizational supports for reform: Implications for improving teaching in schools. Educational Administration Quarterly, 46, 57–95. Google Scholar | SAGE Journals | |
|
Penuel, W., Riel, M., Krause, A., Frank, K. (2009). Analyzing teachers’ professional interactions in a school as social capital: A social network approach. The Teachers College Record, 111, 124–163. Google Scholar | |
|
Penuel, W. R., Frank, K. A., Krause, A. (2006). The distribution of resources and expertise and the implementation of schoolwide reform initiatives. In Proceedings of the 7th international conference on Learning sciences, ICLS ’06 (pp. 522–528). Indiana University, Bloomington, IN: International Society of the Learning Sciences. Google Scholar | |
|
Pitts, V., Spillance, J. (2009). Using social network methods to study school leadership. International Journal of Research & Method in Education, 32, 185–207. Google Scholar | Crossref | |
|
Polak, M., Heiser, W., De Rooij, M., Busing, F. (July 2003). A comparison of correspondence analysis, multidimensional unfolding and the generalized graded unfolding model for single-peaked data. Paper presented at the 13th International Meeting of the Psychometric Society Chia Laguna (Cagliari), Italy. Google Scholar | |
|
R Development Core Team . (2011). R: A language for data analysis and graphics. Vienna, Austria: R Foundation for Statistical Computing. Google Scholar | |
|
Raudenbush, S., Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Thousand Oaks, CA: Sage. Google Scholar | |
|
Snijders, T., Kenny, D. (1999). The social relations model for family data: A multilevel approach. Personal Relationships, 6, 471–486. Google Scholar | Crossref | |
|
Snijders, T., Nowicki, K. (1997). Estimation and prediction for stochastic blockmodels for graphs with latent block structure. Journal of Classification, 14, 75–100. Google Scholar | Crossref | |
|
Spillane, J., Correnti, R., Junker, B.. (2009) Learning leadership: Kernel routines for instructional improvement. (IES Grant Proposal). Chicago, IL: Author. Google Scholar | |
|
Sweet, T., Thomas, A. C., Junker, B. (2012). Handbook on mixed membership models, chapter Hierarchical Mixed Membership Stochastic Blockmodels for Multiple Networks and Experimental Interventions. New York, NY: Chapman & Hall/CRC. invited chapter. Google Scholar | |
|
Templin, J., Ho, M.-H., Anderson, C., Wasserman, S. (2003). Mixed effects p* model for multiple social networks. In Proceedings of the American Statistical Association:Bayesian Statistical Sciences Section, (pp. 4198-4024). Alexandria, VA: American Statistical Association. Google Scholar | |
|
Thomas, S. (2000). Ties that bind: A social network approach to understanding student integration and persistence. The Journal of Higher Education, 71, 591–615. Google Scholar | |
|
Wasserman, S., Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401–425. doi:10.1007/BF02294547 Google Scholar | Crossref | |
|
Weinbaum, E., Cole, R., Weiss, M., Supovitz, J. (2008). Going with the flow: Communication and reform in high schools. In Supovitz, J., Weinbaum, E. (Eds.), The implementation gap: Understanding reform in high schools (pp. 68–102). New York, NY: Teachers College Press. Google Scholar | |
|
Zijlstra, B., van Duijn, M., Snijders, T. (2006). The multilevel p 2 model. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2, 42–47. Google Scholar | Crossref |
