In This Article Expand or collapse the "in this article" section Social Network Analysis

  • Introduction
  • General Overviews
  • Network Data Collection and Quality
  • Applications in Educational Research

Education Social Network Analysis
Brian V. Carolan
  • LAST REVIEWED: 27 October 2016
  • LAST MODIFIED: 27 October 2016
  • DOI: 10.1093/obo/9780199756810-0167


Social network analysis (SNA) operates from the basic premise that relationships matter. This premise informs the many theories, methods, and models that constitute the network perspective. The network perspective has been shaped by theoretical and mathematical insights from numerous disciplines in the natural, social, and behavioral sciences and has only recently been applied more widely to study social phenomena in and around educational settings. Social network analysis is both a set of theoretical perspectives and analytic techniques used to examine how exchanges between individual units (e.g., people) both shape, and are shaped by, the larger context in which those two individual units are embedded. Therefore, social network analysis has been used by educational researchers to examine phenomena ranging from teachers’ adoption of new technologies, the diffusion of attitudes toward school reform among district leaders, and peer influences on the classroom behavior of elementary students. The increased use of social network analysis by educational researchers can be attributed to three factors: (1) a dissatisfaction with attribute-based theories of individuals’ behaviors and attitudes; (2) many educational phenomena are inherently social and therefore appropriately viewed through a network perspective and; (3) the ease with which network data can be collected, visually represented, and analyzed due to advances in computing technologies. Though the scientific study of networks has evolved from numerous disciplines and is now widely employed, a set of core assumptions constitute the network perspective, regardless of the discipline in which it is being used. These foundational assumptions include an emphasis on relations among individuals and not their individual attributes; a focus not on individuals as members of discreet groups but rather as members of overlapping networks, and an examination of relations between individuals in a specific context. For example, a conventional approach to studying the adoption of a reform initiative of a district would likely focus on the openness of individual leaders to new ideas, self-efficacy, and other characteristics. By contrast, a network approach to understanding this same phenomenon would draw attention to the ways in which relations between the district’s leaders and other organizational units influence the adoption process. Thus, these relations bring with them the potential to exchange ideas, expertise, and other tacit knowledge. They may also serve as the means through which influence gets transmitted, therefore affecting the likelihood of the reform being successfully adopted on a broader scale.

General Overviews

The popularity of social network analysis continues to grow rapidly (Watts 2003). This rise in popularity is reflected in the growing number of resources designed to provide a general overview of the models, measures, and methods that constitute the network perspective (Kadushin 2012, Wasserman and Faust 1994). Mirroring the trend in statistics texts, several of these resources provide a general overview of social network analysis while demonstrating how to conduct and interpret common procedures using one of the more popular software applications (Prell 2012). Alternatively, other recent texts provide an introductory overview of social network analysis, with some focusing on its application in a specific field such as communication or public health (Valente 2010), with few dedicated explicitly to education (Carolan 2013). In addition, a number of resources provide a fascinating overview of the historical foundations of social network analysis (Freeman 2004), as well as early examples of its application (Moreno 1993, originally published in 1934). Noteworthy about this history is how it has matured in a way that better links social theory and the quantitative techniques used to examine deductive propositions derived from these theories. Because social network analysis has its roots in a number of disciplines, numerous empirical, theoretical, and mathematical motivations have informed the development of its fundamental concepts, including terms such as network, graph, matrix, tie, relation, and node (Degenne and Forsé 1999). Regardless of the field in which one is working, a solid understanding of these fundamental concepts is necessary and therefore it is strongly recommended that these general texts be consulted. These fundamental concepts make explicit the idea that key features can be identified that distinguish network theory and measurement from the typical data analytical framework common in the social and behavioral sciences.

  • Carolan, B. V. 2013. Social network analysis and education: Theory, methods, and applications. Thousand Oaks, CA: SAGE.

    An introductory textbook that provides numerous examples of how social network analysis can be used to study phenomena that are relevant to educational researchers.

  • Degenne, A., and M. Forsé. 1999. Introducing social networks. Thousand Oaks, CA: SAGE.

    DOI: 10.4135/9781849209373

    One of the earlier overviews of the field that has an especially useful chapter on the utility of graph theory in the representation and analysis of network (relational) data.

  • Freeman, L. C. 2004. The development of social network analysis: A study in the sociology of science. Vancouver, BC: Empirical Press.

    This impressive history from one of the field’s most important figures traces the interdisciplinary development of social network analysis and, in doing so, draws attention its defining characteristics and important insights.

  • Kadushin, C. 2012. Understanding social networks: Theories, concepts, and findings. New York: Oxford Univ. Press.

    A nontechnical introduction to social networks that succinctly describes important foundational concepts and examines why networks are important for an array of social processes, including identity, authority, and diffusion.

  • Moreno, J. L. 1993. Who shall survive? McLean, VA: American Society of Group Psychotherapy and Psychodrama.

    Originally published in 1934. An oft-cited classic that demonstrates the systematic collection and analysis of network data that eventually led to the development of a number of foundational theories, concepts, and techniques including role theory, network, and sociometry.

  • Prell, C. 2012. Social network analysis: History, theory & methodology. Thousand Oaks, CA: SAGE.

    A text that is suitable for both undergraduate and graduate students who need an introduction to the main theories and methods that constitute the social network perspective. Provides numerous examples using a popular social network analysis application, UCINET.

  • Valente, T. W. 2010. Social networks and health: Models, methods, and applications. New York: Oxford Univ. Press.

    DOI: 10.1093/acprof:oso/9780195301014.001.0001

    Though tailored to those interested in public health, readers from other fields will find this introductory text very helpful, especially Part 1, which provides a general introduction to the theories and methods commonly used in social network analysis.

  • Wasserman, S., and K. Faust. 1994. Social network analysis: Methods and applications. New York: Cambridge Univ. Press.

    DOI: 10.1017/CBO9780511815478

    A seminal comprehensive network-specific textbook organized into six parts that discusses the methods for analyzing social networks. Parts 1, 2, and 3 are particularly helpful as they focus on foundational issues, including fundamental concepts, mathematical representations of networks, and definitions and measures for key structural properties.

  • Watts, D. 2003. Six degrees: The science of a connected age. New York: W. W. Norton.

    This nonacademic book by one of the field’s most influential scholars provides a nontechnical introduction to social network analysis by using a range of seemingly unrelated examples to show how individual behavior aggregates to collective behavior.

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