Introduction to social network analysis using UCINET and Netdraw

4 – 8 July 2016

Overview

This is an introductory course, covering the concepts, methods and data analysis techniques of social network analysis. The course is based on the book “Analyzing Social Networks” by Borgatti et al. (Sage) and all participants will be issued with a copy of the book. The course begins with a general introduction to the distinct goals and perspectives of social network analysis, followed by a practical discussion of network data, covering issues of collection, validity, visualization, and mathematical/computer representation. We then take up the methods of detection and description of structural properties, such as centrality, cohesion, subgroups and positional analysis techniques. This is a hands on course largely based around the use of UCINET software, and will give participants experience of analyzing real social network data using the techniques covered in the workshop. No prior knowledge of social network analysis is assumed for this course.

Course objectives

The course will:

1. Introduce the idea of Social Network Analysis

2. Explain how to describe and visualise networks using specialist software (UCINET)

3. Explain key concepts of Social Network Analysis (e.g. Cohesion, Brokerage).

4. Provide hands-on training to use software to investigate social network structure

Course timetable

Day one

Introduction to Social Network Analysis, terminology and the software UCINET/Netdraw.  Chapters 1 and 2

Day two

Morning – Collecting Social Network Data and Research Design. Chapters 3 and 4

Afternoon – Data mangement and Visualization. Chapter 5 and 7

Day three

Morning – Multivariate techniques and whole networks. Chapters 6 and 9.

Afternoon – Centrality and Ego networks. Chapter 10 and 15.

Day four

Morning – Equivalence and core-periphery. Chapter 12

Afternoon – Subgroups and two-mode networks. Chapters 11 and 13

Day five

Morning – Testing hypothesis and Large networks. Chapters 8 and 14.

Chapter numbers refer to the book “Analyzing Social Networks) by Borgatti et al. (Sage).  Timetable is subject to change.

Course presenters

The course will be presented by Martin Everett, Nick Crossley and Elisa Bellotti.

Martin Everett holds a Chair in Social Network Analysis in the School of Social Sciences at the University of Manchester. After he was awarded his DPhil in Oxford, he worked at East London, Westminster and Greenwich universities. He joined the University of Manchester in 2009,  where he helped co-found the Mitchell centre for social network analysis. Martin is a co-author of the software package UCINET and has made significant contributions to methods for social network analysis.

Nick Crossley is professor of sociology at the University of Manchester. His main work using social network analysis has focused upon music worlds, social movements and covert networks. He has also written extensively about ‘relational sociology’, a theoretical position which advocates a focus upon networks in sociology. His most recent book is Networks of Sound, Style and Subversion: the Punk and Post-Punk Worlds of Manchester, London, Liverpool and Sheffield, 1975-1976 (Manchester University Press).

Prior or recommended knowledge/reading/skills

None required but it would be useful to read Scott, J (2000) Social Network Analysis: A Handbook. Sage.

Software to be used

UCINET and Netdraw. It is useful for participants to bring their own laptops running windows (Macs will need to have a PC emulator) and to have downloaded the software in advance.

This can be done for a free period of time from Analytictech website.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s