Papers & Talks
Data Sets & Examples
Modeling Social Networks in Education
June 2014, University of
Sweet, University of Maryland
Andrew Thomas & Brian Junker, Carnegie Mellon University
Beau Dabbs & Sam Adhikari, Carnegie Mellon University
and observational studies in education are sometimes focused not on the
effects of changing curriculum, teaching and learning materials, or
classroom technique, but rather on changes in the way students -- or
teachers, teaching coaches and administrators -- interact with one
another (e.g., relationship between transformational leadership and
leaders' professional social connections, influence of peer group
structure on student behavior and aggression, diffusion of innovation
and reform initiatives in schools, advice giving/receiving and social
capital among teachers, etc.). Many whole school initiatives encourage
some type of social structural change, be it an increase in
collaboration, distribution of leadership or a push toward small
learning communities: in short, they encourage changes in the social
networks of students and of professionals in school systems.
workshop consists of four coordinated sessions, and free time for
problem solving and discussion, spread over two days:
- A brief introduction to R
- An introduction to social network
data, using R
- The CIDneworks package in R, for
modeling a single social network
- The HLSM package in R, for modeling
hierarchical latent space models (HLSMs) for social networks in
are a subfamily of the class of Hierarchical
Network Models (HNMs). These models enable you to model and
detect the effects of
interventions and other covariates on the structure of social networks,
by pooling information across ensembles of social networks (teachers’
professional networks across multiple school buildings, students’ peer
networks across multiple geographic areas, etc.).
walk you through hands-on data analyses, using software we have
developed, to model and understand influences on social network
structure in education settings. A brief summary of the
aspects of HNMs will be included but most of the workshop will focus on
substantive research questions and useful interpretations.
analyses will be conducted in the free statistical package R;
participants should bring a laptop with the current version of R
installed and functioning correctly. Other data and software
be provided in the workshop.
more information about the workshop, or to find out what will be
covered in the R bootcamp, please see 2014-06
wishing to brush up on R in advance may consult the websites
and/or the books
- Teetor, P. (2011). R cookbook.
- Chang, W. (2012). R graphics
SOME SUGGESTED READINGS:
S. and Bauman, K. (1993). Peer group structure and adolescent
cigarette smoking: a social network analysis.
Health and Social Behavior, 226--236.
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,
- Low, S., Polanin, J.R., Espelage,
(2013). The Role of Social Networks in Physical and
Aggression Among Young Adolescents. Journal of
Adolescence, 42(7), 1078--1089.
- 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).
International Society of the Learning Sciences.
J., Kim, C., and Frank, K. (2011). Instructional Advice and
Information Providing and Receiving Behavior in Elementary
Schools: Exploring Tie Formation as a Building Block in
Capital Development. Institute for Policy Research,
Northwester University, Working Paper Series.
T. M., Thomas, A. C., and Junker, B. W. (2013) Hierarchical
network models for education research: hierarchical latent
models. Journal of Educational and Behavioral Research, 33,
- Thomas, A., Dabbs, B., Sadinle, M,
T., & Junker, B. (June 2013). Conditionally
network models; an integrative framework for modeling and
computing. Working paper.
- Thomas, A., Dabbs,
B., & Sweet, T. (August 2013). CIDnet: An R
package for inference with conditionally independent dyad
models. In preparation.
- Weinbaum, E., Cole, R.,
Weiss, M., & Supovitz, J. (2008). Going with the
Communication and reform in high schools. In J. Supovitz &
E.Weinbaum (Eds.), The implementation gap: understanding
in high schools (pp. 68--102). Teachers College Press.