Bachelor of Arts (BAEcon)

BAEcon Development Studies

In-depth study into the problems and options faced by the developing world.

  • Duration: 3 or 4 years
  • Year of entry: 2025
  • UCAS course code: L900 / Institution code: M20
  • Key features:
  • Study abroad
  • Industrial experience

Full entry requirementsHow to apply

Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £29,500 per annum. For general information please see the undergraduate finance pages.

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Scholarships/sponsorships

Scholarships and bursaries, including the Manchester Bursary , are available to eligible home/EU students.

Some undergraduate UK students will receive bursaries of up to £2,000 per year, in addition to the government package of maintenance grants.

You can get information and advice on student finance to help you manage your money.

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

Pre/co-requisites

Basic knowledge of statistical analysis.

Aims

The unit aims to:  

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

Teaching and learning methods

The course involves lectures and computer workshops. 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 workshops are linked to the lectures and serve to give a concrete and hands-on Shapeperspective on the material taught. Furthermore, the workshops give students training in specific methodologies and embed practical skills. The workshops 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 network analysis, both theoretical and technical.

Intellectual skills

Relate concepts such as micro-macro, self-organisation, structuring mechanism, 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 (essay) 100%

The word count must not exceed 2000 words. The essay must include a (1) network visualization and tables with (2) descriptive statistics, (3) statistical model and goodness of fit test, (4) interpretations of 1-3.

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
Yan Wang Unit coordinator

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