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Emirbayer and Goodwin’s 1994 survey of the state of social network analysis is thick. And long. Though critical, Emirbayer and Goodwin clearly approach network analysis with optimism; they label the increasing use of network analysis as the second transformative movement in sociological practice (1417). The authors pull out several of the assumptions underlying network analysis.

  1. Relationships influence behavior (1417). Later the authors expand on this notion when describing “structural equivalence” (1422), a theory which says that one’s place in the network is the best predictor of behavior.
  2. Social networks can be analyzed on multiple levels. One can look at an individual node (ego network) or look at the whole network.
  3. The structure of the network can explain group dynamics (1419). For example, more densely connected networks represent high social capital as many nodes have access to many different sources of information.

The authors then discuss what they see as three different approaches to network analysis: structural determinism, structural instrumentalism, and structural constructivism. Structural constructivism holds the highest rank for Emirbayer and Goodwin, because it best acknowledges the tension between individual agency and cultural context.

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In another class, I am working on applying social network analysis to some MOOC forum data. For that class, I read about a study which analyzed interactions on Blackboard (similar to Moodle). The authors of the study argue that online student forums with many “bridges,” students who connect otherwise disconnected groups, showcase more creativity. Thus, educators should work to identify and support bridging individuals.

However, I question the universal applicability of the study. In other words, like Emirbayer and Goodwin I think we need to put social networks in context (1441). Emirbayer and Goodwin’s emphasis on both individual agency and cultural context are extremely relevant to this educational data.

Context: How students use networks will depend on what instructions they are given. Also, at schools like Davidson where small classes facilitate rich in-person discussion, students may not be as motivated to weigh in online (aside from teacher instruction). Thus, the network yielded from the forum data will not reflect much.

Individual response: The study’s findings pivot on the understanding that “agility” in the social network represents creativity. I have a hard time believing, though, that the same students who are bridges in the classroom are also bridges online. A person’s affinities don’t neatly track to whatever medium the researcher chooses to explore.

Moral of the story: Social network analysis is a powerful tool, but employing it requires the researcher to keep asking questions to more deeply understand both individual motivation and cultural context.