Postgraduate Course: Analysing Social Networks with Statistics (PGSP11452)
Course Outline
| 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 |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The course introduces students to statistical methods for analysing social networks. It is organised through a combination of classroom teaching and hands-on computer work. Using the statistical environment R, the course will first cover exploratory Social Network Analysis (SNA) before progressing into more advanced statistical methods. |
| Course description |
The course introduces students to statistical methods for analysing social networks. While Social Network Analysis (SNA) has long been used as an exploratory method, hypothesis testing and statistical models with network data are increasingly popular methods in social science. They require specific statistical techniques.
The course will have a practical focus and will introduce students to a range of basic and more advanced network analysis methods through hands-on computer work. Through lectures and readings, students will learn key concepts and measures of social network research. In labs, students will apply this knowledge through exercises with real-world network datasets using the statistical environment R. The course will first cover exploratory Social Network Analysis (SNA) before progressing into more advanced statistical methods.
By the end of the course, students will be able to visualise and analyse networks in R, know different methods to test hypotheses with network data, use exponential random graph models (ERGMs) for modelling social networks, handle large samples of ego-centric networks (personal networks) and analyse them using single- and (if time permits) multi-level modelling.
These methods will enable students to address the research questions they will consider in their final essay. Here are some examples of the kinds of hypotheses students will be able to test at the end of the course:
- Are women significantly more central than men within Facebook networks?
- Are friendship ties more likely between people from the same social class?
- What factors at the individual, tie and network levels predict the probability of a tie in a network?
Outline content:
- What is the network approach?
- Network theories and the social capital approach
- Starting with SNA in R: Importing, visualising and transforming network data
- Analysing the network cohesion and detecting communities
- Analysing network positions: centrality measures
- Scraping and analysing Twitter network data
- Statistical testing and regression analysis with network data using permutation-based methods
- Exponential random graph models
- Ego-centric network analysis: single- and multi-level modelling with personal networks
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 PG option of the course is also offered to PG students with appropriate background knowledge in statistics.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 15 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Two 750-word exercises, worth 40% of total mark
One 3,000-word essay (appendices and bibliography excluded), worth 60% of total mark.
The essay will address a research question briefly situated in scientific literature using network data, exploratory SNA (social network analysis) and a (set of) statistical model(s). The essay will include a substantive introduction, a short literature review, a methodology section and an appropriate SNA, a substantive discussion of the results and a conclusion. |
| Feedback |
The course will include formative feedback through weekly exercises. The goal of the weekly exercises is to encourage students to participate actively in Computer Lab sessions. The exercises will be technically oriented to ensure that students have a good command of the software packages. Nevertheless, students will be encouraged to reflect on the short lectures and readings to discuss the results.
Three exercises will be assessed during the course, providing students with feedback for their final essay.
The three exercises will be returned with feedback within 15 working days of submission. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use the statistical environment 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
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Reading List
Agneessens, F. (2020). Dyadic, nodal and group-level approaches to study the antecedents and consequences of networks: Which social network models to use and when. In: Light, R. Moody, J. The Oxford Handbook of Social Networks. Oxford University Press.
Borgatti, S. P., Everett, M. G., Johnson, J. C. Agneessens, F. (2022). Analyzing Social Networks Using R. Sage.
Borgatti, S. P. Everett, M. G. Johnson, J. C. (2018). Analyzing social networks. 2nd Edition. Sage.
Crossley, N. Bellotti, E. Edwards, G. Everett, M. G. Koskinen, J. Tranmer, M. (2015). Social network analysis for ego-nets. Sage.
Kadushin, C. (2011). Understanding social networks: Theories, concepts, and findings. Oxford University Press,
Luke, D. A. (2016). A User's Guide to Network Analysis in R. Springer.
McCarty, C. Lubbers, M. J. Vacca, R. Molina, J. L. (2019). Conducting personal network research: A practical guide. Guilford Publications.
Prell, C. (2012). Social network analysis: History, theory and methodology. Sage.
Robins, G. (2015). Doing social network research. Sage.
Scott, J. (2017). Social network analysis. 4th Edition. Sage.
Yang, S. Keller, F. B. Zheng, L. (2017). Social network analysis: Methods and Examples. Sage. |
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 | Mr James Wills
Tel:
Email: jwills2@ed.ac.uk |
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