BASS Politics and Data Analytics / Course details

Year of entry: 2024

Course unit details:
Network Analysis

Course unit fact file
Unit code SOST30022
Credit rating 20
Unit level Level 3
Teaching period(s) Semester 2
Available as a free choice unit? Yes

Aims

The unit aims to:  

  1. Introduce a toolbox for empirical investigation of theories on social interaction and complexity. 
  2. Introduce the practical issues involved in managing and analysing network data. 
  3. Provide a theory- and research-driven perspective on everyday observables and the skills and knowledge to solve analytical puzzles in a wide array of applied contexts. 
  4. Give students a working handle on the basic analysis tools. 
  5. Foster familiarity with analytical tools and methods at a level that enables students to further their skills in relevant areas. 
  6. Offer an analytical framework for critical appraisal of quantitative statements in social networks and related areas. 

Teaching and learning methods

The course involves lectures and computer tutorials. The lecture component provides theoretical and methodological frameworks for learning about the analysis of social network data and the key pathways from theory to subjecting research questions to empirical scrutiny. The tutorials are linked to the lectures and serve to give a concrete and hands-on Shapeperspective on the material taught. Furthermore, the tutorials give students training in specific methodologies and embed practical skills. The tutorials have an immediate goal of equipping students with the necessary skills and knowledge to complete the assignment. Blackboard resources are used to enable students to access teaching data and data sources. Students are also provided with video materials of lectures and software tutorials. 

Knowledge and understanding

Understand the empirical requirements and evidence needed for drawing conclusions about complex social processes involving network structures. Operate with fundamental concepts in social network analysis, both theoretical and technical.

Intellectual skills

Relate concepts such as micro-macro, self-organisation, and emergence to specific predictions and hypotheses for observables on network data. Choose the appropriate network-analytical approach for a particular set of relevant research questions.  

Practical skills

  • Manage social network datasets and analyse network data with dedicated network-analytical software.  
  • Visualise, describe, and report the results of social network analysis, drawing conclusions about related social processes.  
  • Apply essential network-analytical concepts. 

Transferable skills and personal qualities

Handle network data, interpret analytical results, and report them.

Assessment methods

Written assignment 100%

The word count must not exceed 2000 words. The essay must include a (1) network visualization and tables with (2) descriptive statistics and (3) models' parameters tests.

Feedback methods

All Social Statistics courses include both formative feedback - which lets you know how you're getting on and what you could do to improve - and summative feedback - which gives you a mark for your assessed work. 

Recommended reading

Borgatti S., Everett M, Johnson J. (2018). Analysing Social Networks 2nd Ed, Sage, London 

Hanneman R.A. and Riddle M. (2005). Introduction to Social Network Analysis. Available at https://faculty.ucr.edu/~hanneman/nettext/ 

Lusher,D., Koskinen, J., and Robins, G. (2013). Exponential random graph models for social networks: Theory, methods and applications. Cambridge University Press 

Robins, G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage.  

Scott, J. (2000) Social Network Analysis: A Handbook, London, Sage 

Wasserman, S. and Faust, K. (1994) Social Network Analysis, Cambridge University Press 

Online Resources:

Mitchell Centre www.ccsr.ac.uk/mitchell  

Methods@Manchester www.methods.manchester.ac.uk/  

Study hours

Independent study hours
Independent study 170

Teaching staff

Staff member Role
Nikita Basov Unit coordinator

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