SOC 6410 - [Methods for Social Network Analysis]

Fall. Next offered 2016-2017. 4 credits.

M. Brashears.

It will introduce graduate students to the methodology of social network analysis. We will begin by discussing the different types of network data before exploring how these data are collected, and the strengths and limitations of each resulting data structure. We will then learn about methods of data cleaning and the particular complications that non-response or truncated responses can introduce to network studies. Finally, we will introduce several types of analytic approaches including ego-network data analysis, MRQAP, Stochastic Actor-Oriented Models (Siena), and Exponential Random Graph models (ERGMs). Familiarity with R will be helpful but is not required. Students will be expected to develop their own network project throughout the semester, potentially collecting their own data.