methods@manchester summer school 2019 - Introduction to Social Network Analysis

Date:

01/07/2019 - 05/07/2019

Organised by:

methods@manchester

Presenter:

Dr Elisa Bellotti, Professor Nick Crossley and Professor Martin Everett

Level:

Entry (no or almost no prior knowledge)

Contact:

methods@manchester
email: methods@manchester.ac.uk
telephone: 01612754269

Map:

View in Google Maps  (M13 9PL)

Venue:

The University of Manchester
Oxford Road, Manchester

Description:

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" 2nd ed 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:

· Introduce the idea of Social Network Analysis

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

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

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

 

Prerequisites
None

 

Cost:

Students - £600
University of Manchester Staff - £600
Other attendees - £900

Website and registration:

Region:

North West

Keywords:

Network analysis, UCINET software

Related publications and presentations:

Network analysis

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