9am - 5:30pm
Tuesday 7 May - Thursday 9 May 2019
Social Network Analysis
This course introduces you to various concepts, methods, and applications of social network analysis drawn from the social sciences.
‘One of the most potent ideas in the social sciences is the notion that individuals are embedded in thick webs of social relations and interactions’ (Borgatti et al. 2009, p. 892). Social network analysis helps us understand individuals’ contact with the larger social world. It focuses on relationships between social entities, and analyses patterns of social interaction and their influence on individual behaviour. For instance, social network analysis is used to study the role of social capital in getting better jobs; the spread of infectious diseases, ideas, and ‘fake news’; criminal collaboration in terrorist and organised crime groups. It also helps us understand the impact of personal networks on individual behaviour, e.g. whether adolescents with many smokers among their friends are more likely to start smoking.
This course introduces you to various concepts, methods, and applications of social network analysis drawn from the social sciences. We will start with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. We will then consider descriptive network- and individual-level statistics and their applications in social science research. Finally, we will discuss methods for testing hypotheses about social network structure, and introduce models for social networks. The emphasis will be on applying social network analysis theories and methods to real-world data, and on understanding and interpreting results, rather than on the underlying mathematics. The course will also provide training to use software to investigate social networks.
Indicative content includes:
- Historical and theorietical foundations
- Data sources and data collection strategies
- Graphs, matrices, and sociograms
- Centrality and centralisation
- Balance, reciprocity, and transitivity
- Density and cohesive subgroups
- Equivalence and blockmodels
- Dyads and triads
- Statistical models for social networks
On successful completion of this course, you will be able to:
- Describe social network analysis concepts, data collection strategies, and analytic techniques (C,K)
- Have a critical understanding of the key network data collection strategies and their potential limitations (C,K)
- Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results (C,P)
- Be able to implement a social network analysis on real world data and critically evaluate the results (C/P)
- Engage with different applications of social network analysis in the social sciences (C,T)
- Use social network analysis software to analyse network data (T,P)
Key: C-Cognitive/Analytical; K-Subject Knowledge; T-Transferable Skills; P- Professional/ Practical skills
Dr Berlusconi is a criminologist with particular expertise in social network analysis and its application to the study of co-offending and illicit markets.
No prior knowledge is required. Familiarity with basic mathematical notation and standard statistical methods is an advantage but not essential. All computing workshops will be in R, using the GUI RStudio. Familiarity with R is therefore an advantage. For a basic introduction to R for data manipulation and analysis, please click here.
Level of study
Entry (no or almost no prior knowledge)
Varies according to status:
- £595 - Government/commercial sector
- £495 - Educational/charitable sector
- £395 - Students.
Borgatti, S.P., Mehra, A., Brass, D.J. & Labianca, G. (2009). “Network Analysis in the Social Sciences.” Science 323: 892-895.
Participants on the course will include some students completing the MSc in Social Research Methods