Undergraduate Course: Analysing Social Networks with Statistics (SSPS10029)
|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
|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.
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?
- 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
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Statistical Modelling (SSPS10027)
||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
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Supervised Practical/Workshop/Studio Hours 10,
Feedback/Feedforward Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Three 750 word exercises, worth 40% of total mark
One 2,500 word essay (appendices and bibliography excluded), worth 60% of total mark.
||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.
The assessment of the weekly exercises provide students with formative feedback for their final essay.
Weekly exercises will be returned with feedback within 15 working days of submission.
|No Exam Information
On completion of this course, the student will be able to:
- Use appropriate SNA software packages 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
|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.
|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||Mr Ethan Alexander
Tel: (0131 6)50 4001