COURSE OVERVIEW

Social Network Analysis is an approach that allows researchers to quantify the pattern of relations among a set of actors. The actors are usually people, but they can also be animals, organizations, or nations. Social networks analysis can be applied to substantive problems that cut across many subjects and disciplines. Any research problem where social context is important may benefit from social network analysis.

There are two distinct types of social network analysis. “Sociocentric” or “whole” networks consist of the relations between all actors within a group. These actors might be members of a gang, a classroom of children, a village or the board of directors of a Fortune 500 company. The focus of sociocentric network studies is the structure within the group. “Egocentric” or “personal” networks consist of the people known by individuals. Typically, respondents are presented with questions or cues that elicit the names of people they know. These data are analyzed by summarizing the characteristics of the network “alters” for each respondent and correlating aggregate characteristics with the individual characteristics of the respondents themselves. Data like these enable the researcher to study topics such as the characteristics of social support networks, or the relationship between IV drug use and the transmission of HIV.

In this course you will be introduced to the research methods and theoretical approaches used in both types of social network analysis. The first part of the course focuses on an overview of social network analysis, including fundamental concepts such as cohesion, bridging, directed versus undirected ties, strength of tie, structural holes, one mode and two mode data. You will learn about specific types of social network metrics that are used to describe these concepts and test them against outcome measures that may interest you. You will also learn about social network visualization, which is a way to combine both the composition and structure of the network so that you can quickly identify patterns that are more difficult to find using metrics alone.

The remainder of the course focuses on hands on data collection and analysis using social network tools. These include Ucinet for whole network analysis and Egonet for personal network analysis. You will learn how to construct a social network questionnaire and how to identify methods for collecting whole network data from existing data (such as e-mail, citations or just plain observation). During the course you will collect data from each other for both whole networks and your own personal networks. These will be analyzed in class so that you will better understand the benefit of these methods and measures.

COURSE REQUIREMENTS

All participants must have laptop that can run Windows-based software programs. (Mac users may need to install software to emulate a Windows environment.) Participants will need to have Microsoft Word and Excel loaded on their computers and will be asked to download and install additional demo or free software before arriving at methods camp. Participants should also install Java on their computers prior to the class. This is available for free from www.java.com (click Free Java Download).

COURSE SCHEDULE

READINGS