'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, pp.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, for example, whether adolescents with many smokers among their friends are more likely to start smoking.
This course will introduce you to various concepts, methods and applications of social network analysis drawn from the social sciences. You will begin with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. You will then consider descriptive network-level and individual-level statistics and their applications in social science research. Finally, you 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. You will also receive training to use software to investigate 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 and K)
- Have a critical understanding of the key network data collection strategies and their potential limitations (C and K)
- Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results (C and P)
- Be able to implement a social network analysis on real world data and critically evaluate the results (C and P)
- Engage with different applications of social network analysis in the social sciences (C and T)
- Use social network analysis software to analyse network data (P and T)
- Historical and theoretical 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
Borgatti, S.P., Mehra, A., Brass, D.J. and Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323, pp.892-895.
Maximum of 25 people. Please note, 10 participants will be students from our MSc Social Research Methods course.
Software and equipment
There are no formal entry requirements for this course.
Fees and funding
Fees are to be confirmed
How to apply
Applications for this course are currently closed.
In light of the COVID-19 pandemic and following the latest advice from the government relating to large gatherings, we have taken the decision to cancel future dates for this course, until further notice. Those who have registered for this course will be contacted directly regarding refunds. Please bear with us as this may take up to 14 working days to process. We apologise for any disappointment or inconvenience caused.
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