Postgraduate Course: Analysing Social Networks with Statistics (PGSP11452)
|School||School of Social and Political Science
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||The course enables students to use statistical tools to analyse social network data. While Social Network Analysis (SNA) has long been used as an exploratory method, hypothesis testing and estimation techniques with network data is becoming an increasingly popular method in social science that require specific statistical techniques.
The course enables students to use statistical tools to analyse social network data. While Social Network Analysis (SNA) has long been used as an exploratory method, hypothesis testing and estimation techniques with network data is becoming an increasingly popular method in social science that require specific statistical techniques.
The course will have a practical focus and will introduce students to a range of basic and more advanced statistical models through hands-on computer work. These techniques will enable students to test the research questions (hypotheses) they will consider in their dissertation work. Students will also learn how to analyse network dynamics and large samples of ego-networks (personal networks) using single- and multi-level modelling.
- Are women significantly more central than men within Facebook networks?
- Is support within postżdivorce families more likely between people having blood relationships?
- Are friendship ties more likely between people from the same social class?
- What factors explain the persistence of ties before and after migration within personal networks of migrants?
- Are structural factors (e.g. density of connection) more important than individual (country of origin) or relational factors (tie strength)?
These (and others) are the kind of questions that students will be able to test at the end of the course.
- Starting with UCINet: Importing, visualising and transforming social network data
- Analysing the network cohesion: density, reciprocity and transitivity
- Analysing power and prestige: centrality measures
- Detecting communities: cohesive subgroup analysis
- Analysing affiliation (two-mode) networks
- Analysing network data with R and statnet
- Statistical testing and regression analysis with network data using permutation-based methods
- Ego-network analysis with R
- Introduction to multilevel modelling with ego-networks
- Introduction to longitudinal models: Analysing network dynamics with RSiena
This is a course for Q-Step degree programmes Social Policy with Quantitative Methods, Sociology with QM, Politics with QM and International Relations with QM. It is also open to all other Honours students who have successfully completed Doing Social Research with Statistics (SSPS08007) or any equivalent statistical course. The course can also be offered to PG students who have completed Social Network Analysis: Mapping and Exploring the Network Society (PGSP11372) and PG students with appropriate background knowledge in statistics.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Use the software packages UCINet and R to analyse social networks, using both an exploratory approach and statistical models
- Test scientific questions in a network perspective
- Design and conduct a small-scale research project using SNA in a chosen discipline and write a detailed report to communicate the results
- Have a critical understanding of the capacity to embark on SNA for future research
|Acton, R. M. Jasny, L. (2012). An introduction to network analysis with R and statnet. Sunbelt XXXII Workshop Series.|
Borgatti, S. P. Everett, M. G. Johnson, J. C. (2013). Analyzing social networks. Sage.
Crossley, N. Bellotti, E. Edwards, G. Everett, M. G. Koskinen, J. Tranmer, M. (2015). Social network analysis for ego-nets. Sage.
Hanneman, R. A. Riddle, M. Introduction to social network methods. http://faculty.ucr.edu/~hanneman/nettext/
Kolaczyk, E. D. Csárdi, G. (2014). Statistical analysis of network data with R. Springer.
Ripley, R. M., Snijders, T. A. B. Boda, Z. Vörös, A. Preciado, P. (2014). Manual for RSiena. University of Oxford, Department of Statistics, Nuffield College.
|Graduate Attributes and Skills
||Developing advanced SNA techniques and the capacity to use them in applied scientific context.
|Course organiser||Dr Gil Viry
Tel: (0131 6)51 5768
|Course secretary||Ms Cath Thompson
Tel: (0131 6)51 3892