Social network anlaysis has found utility is institutional, classroom and analyses of networked data in socially-based educational games. However, the utility of the method largely rests on being able to ascribe meaning to the structure of the network. Without meaningful interpretation of structure there is no added value to a networked model. Understanding measures of centrality and network structure in SNA are therefore a crucial, though difficult, aspect of the method.
- Generate sociogram
- Apply and interpret a centrality measures in a network
- Apply and interpret clustering procedures in a network
In this project, I build several social network diagrams (graphs/sociograms) of a school classroom and then analyze centrality measures and clusters within the network. In addition, I also analyze multiplex organizational network data from a law firm collected from Lazega (2001) and publically accessed through The Colorado Index of Complex Networks (ICON). In the law firm network, I compare differences in network types, including advice, co-working, and friendship networks. I also examine how network structure is moderated by demographic variables (e.g. age and gender) and functional practice areas (e.g. litigation, corporate).
Lazega, E. (2001). The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership. Oxford University Press.
Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robins, and Mark S. (2006). New specifications for exponential random graph models. Sociological Methodology, 99-153.