Essex Summer School – Introduction to SNA (13 – 24 July 2020)

Introduction to Social Network Analysis (35 hours)

Course Content
This course will provide a practical, but comprehensive introduction to the analysis of social networks. Social network analysis takes the view that social research should not solely focus on the individual unit of analysis, but rather emphasises that researchers should also incorporate the social relations (networks) that connect these individual units (actors). For example, we might be interested in friendship among schoolchildren, trust among employees, collaboration among NGOs, exchanges of resources among companies, or conflict among nations.

The course focuses on the description and visualisation of social network data using UCINET. We will concentrate on uncovering structural properties of the network (e.g. density, homophily, and clustering), as well as on how to identify important persons in a network (e.g. degree centrality, structural holes, …). We will also pay attention to the detection of subgroups and deal with basic hypothesis testing for social network analysis. Throughout the course some classic theories that focus on network processes (e.g. related to homophily, centrality measures, structural holes, Granovetter’s strength of weak ties and small worlds) will be discussed.

Course Objectives
Participants will obtain a thorough understanding of the main theories and (basic) methods of social network analysis. Having taken this module, students should be able to design and carry out a social network research studies, as well as be able to interpret network analyses in a consultancy setting.

Course Prerequisites
Participants need to be familiar with basic mathematical notation provided in an elementary introductory statistics module (e.g. know when to reject a null hypothesis and be able to read a regression output). Emphasis is on understanding and interpretative methods, not on the underlying mathematics. Participants should also be comfortable learning new menu-driven software of complexity, such as Microsoft Excel.
Representative Background Reading
Scott, J. 2000. Social Network Analysis. Newbury Park CA, Sage.

Required Reading

Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage. (Please note that this book will be provided by the summer school on arrival).

Click here to go to the Essex Summer School website.