People and institutions involved
IES program contact(s)
Products and publications
Book chapter
Sweet, T. M. (2017). Modeling Collaboration with Social Network Models. In von Davier A., Zhu M., Kyllonen P. (Eds) Innovative Assessment of Collaboration (pp. 287-302). Springer, Cham.
Sweet, T.M., Thomas, A.C., and Junker, B.W. (2014). Hierarchical Mixed Membership Stochastic Block Models for Multiple Networks and Experimental Interventions. Handbook on Mixed Membership Models and Their Applications. CRC Press: Taylor and Francis Group.
Journal article, monograph, or newsletter
Hopkins, M., Lowenhaupt, R., and Sweet, T.M. (2015). Organizing English Learner Instruction in New Immigrant Destinations: District Infrastructure and Subject-Specific School Practice. American Educational Research Journal, 52(3): 408-439.
Spillane, J. P., Hopkins, M., and Sweet, T. (2015). Intra- and Interschool Interactions about Instruction: Exploring the Conditions for Social Capital Development. American Journal of Education, 122(1): 71-110.
Sweet, T. M. (2015). Incorporating Covariates into Stochastic Blockmodels. Journal of Educational and Behavioral Statistics, 40(6): 635-664.
Sweet, T. M., Thomas, A. C., and Junker, B. W. (2013). Hierarchical Network Models for Education Research: Hierarchical Latent Space Models. Journal of Educational and Behavioral Research, 38(3): 295-318.
Sweet, T.M., and Junker, B.W. (2016). Power to Detect Intervention Effects on Ensembles of Social Networks. Journal of Educational and Behavioral Statistics, 41(2): 180-204.
Sweet, T.M., and Zheng, Q. (2017). A Mixed Membership Model-Based Measure for Subgroup Integration in Social Networks. Social Networks, 48, 169-180.
Supplemental information
In this project, researchers will carry out a methodological investigation of social network analysis and examine its potential applicability in education through the development of hierarchical network models (HNMs). HNMs can be used to model multiple, partially-exchangeable social networks (such as the professional communities that teachers form in multiple different school buildings), incorporate treatment and other effects, and accommodate the usual nesting/cluster structure that hierarchical linear models handle in other contexts. In addition to conducting simulation studies of issues pertinent to statistical power and model estimation, the researchers will use hierarchical network models to analyze available social network data, including data from the National Longitudinal Study of Adolescent Health. Products of the research will be peer-reviewed publications and user-friendly open-source code for conducting analyses with HNM.
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
