Undergraduate Course: Analysing Social Networks with Statistics (SSPS10029)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| 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 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 post-divorce 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.
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| Course description |
1. Starting with UCINet: Importing, visualising and transforming social network data
2. Analysing the network cohesion: density, reciprocity and transitivity
3. Analysing power and prestige: centrality measures
4. Detecting communities: cohesive subgroup analysis
5. 'Introduction to using Twitter network data in R
6. Analysing network data with R and statnet
7. Statistical testing and regression analysis with network data using permutation-based methods
8. Ego-network analysis with R
9. Introduction to multilevel modelling with ego-networks
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Modelling (SSPS10027)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For those students who are required to take a Quantitative Methods course as part of their degree programme, this course can be counted towards that condition. |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Use the software packages UCINet and R to analyse social networks
- Test scientific questions in SNA terms.
- Undertake analysis of network data using statistical models
- Plan, design and execute a study using real-world network data and SNA in one area linked to a discipline of their choosing
- Interpret and communicate the results of exploratory and confirmatory SNA clearly
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Reading List
- 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.
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Additional Information
| Graduate Attributes and Skills |
Developing advanced SNA techniques and the capacity to use them in applied scientific context. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Gil Viry
Tel: (0131 6)51 5768
Email: Gil.Viry@ed.ac.uk |
Course secretary | Miss Katarzyna Pietrzak
Tel: (0131 6)51 3162
Email: K.Pietrzak@ed.ac.uk |
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