Social networks, centrality, UCINET and social capital.
Facilitator/s:
Martin Everett
Workshop Description:
Social network analysis is an important tool for trying to measure different aspects of social capital. In this workshop we will explore how the software package UCINET can be exploited to help us fulfil this goal. This interactive workshop gives all participants an opportunity for hands-on experience analyzing network data using the UCINET/Netdraw software package. In the first part we will provide a beginner’s tutorial on the concepts, methods, and data analysis techniques from data entry through reporting results. Together, we will use sample datasets to focus on the interpretation and calculation of some of the most common measures of network analysis at the node, dyad, and whole-network level of analysis. We will also provide a hands-on tutorial for NetDraw, which creates network visualizations. We will then look in closer detail at centrality measures, explaining there assumptions and interpretation. In addition we will look at ego network measure in particular Burt’s theory of structural holes and related measures.
Objectives or Learning Outcomes:
Intended Audience:
We assume little or no knowledge of network methods but it would be useful to have had exposure to the terminology. It would be beneficial if participants had UCINET on their laptops so they can gain hands on experience. Note UCINET is free and can be downloaded from
Note this only runs on a PC so Mac users will need an emulator such as parallels.
About the Facilitator/s:
Martin Everett is Professor of Social Network Analysis and co-director of the Mitchell Centre for Social Network Analysis at the University of Manchester He holds a BSc in mathematics from Loughborough University, an MSc from Oxford University, and a DPhil jointly awarded by mathematics and sociology from Oxford, where he was supervised by Clyde Mitchell, a founding figure in social network analysis. With Stephen Borgatti, he co-authored UCINET, a widely-used software package for social network analysis, and edits the journal Network Science. He has published over 100 peer-reviewed articles and consulted with government agencies and private companies.