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Semester 2

Analysing Social Networks with Statistics (SSPS10029)

Subject

Social and Political Studies

College

CAHSS

Credits

20

Normal Year Taken

4

Delivery Session Year

2023/2024

Pre-requisites

Visiting students must have completed at least 2 Social Science courses (i.e. Sociology, Politics, Social Policy, Social Anthropology, African Studies, American Studies, Gender/Queer Studies) at grade B or above, including a course equivalent to Statistical Modelling (SSPS10027). We will only consider University/College level courses. Please see additional restrictions below.

Course 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.

Assessment Information

Written Exam 0%, Coursework 100%, Practical Exam 0%

Additional Restrictions

Students cannot take this course alongside Statistical Modelling (SSPS10027).

view the timetable and further details for this course