SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to
Social Network Analysis Summer School Courses
methods@manchester Summer School 1-12 July 2019
University of Manchester
Limited PhD Bursaries Available
methods@manchester is delighted to be holding its annual Summer School from 1-12 July 2019.
The Summer School offers a range of specialised courses covering a variety of topics that are particularly relevant to postgraduate and ECR research in humanities. The selection includes software training as well
as qualitative and quantitative analysis. The course content is based on approaches from across the various schools in the Faculty of Humanities at the University of Manchester.
Each Summer School course will run for one week, delivering four days of content to a five-day timetable (Monday afternoon to Friday lunch-time), building on successful methods@manchester and CMIST short-courses
given throughout the year.
We have a small number of subsidised places for PhD students, reducing the cost of a course to £300*. To apply or for further details please email contact
[log in to unmask] for an application form confirming the course you are applying for.
*with the exception of Introduction to Longitudinal Data Analysis using R which will be reduced to £375.
- Introduction to Social Network Analysis using UCINET and Netdraw (1-5 July 2019)
- Advanced social network analysis (8-12 July 2019)
Further information on the courses is set out below.
Introduction to Social Network Analysis using UCINET and Netdraw (1-5 July 2019)
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,
visualisation, 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
on the use of UCINET software and will give participants the experience of analysing real social network data using the techniques covered in the workshop. No prior knowledge of social network analysis is assumed for this course.
Advanced social network analysis (8-12 July 2019)
An introduction to statistical analysis of networks and some advanced concepts building on the introductory course. To benefit fully from the course requires a basic knowledge of standard statistical methods,
such regression analysis. The course aims to give a basic understanding of and working handle on drawing inference for structure and attributes for cross-sectional data. A fundamental notion of the course will be how the structure of observed graphs relate
to various forms of random graphs. This will be developed in the context of non-parametric approaches and elaborated to the analysis of networks using exponential random graph models (ERGM) and permutation tests. The main focus will be on explaining structure
but an outlook to explaining individual-level outcomes will be provided.
The participant will be provided with several hands-on exercises, applying the approaches to a suite of real-world data sets. We will use the stand-alone graphical user interface package
[TS1] and R as well as other specialist sna software Eg Visone and UCINET. In R we will learn how to use the packages ‘sna’ and ‘statnet’. No familiarity with R is assumed but preparatory exercises will be provided ahead of the course.
Tel: 0161 275 4269