Fees and funding

Fees

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

  • MSc (full-time)
    UK students (per annum): £15,200
    International, including EU, students (per annum): £31,500

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

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

We know that student finance can be complicated. The links below provide further information to help guide you.

Course unit details:
Research Skills for Economic Development 1 (Quantitative Methods)

Course unit fact file
Unit code MGDI60301
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Global Development Institute
Available as a free choice unit? No

Overview

The course unit covers theory, relevant applications and practical exercises in computer workshops. Much emphasis is placed on developing experience and confidence in the use of statistical software. Empirical work, using alternative forms of regression analysis, forms an increasingly important foundation for policy analysis and evidence based policy advice in economics and other branches of the social sciences. Therefore, acquiring competence in this field through the course will be of great value to development practitioners as well as those aiming for academic and other development-related careers.

Aims

The unit aims to enable students through development of theoretical insights and practical skills to become:

  • Competent users of statistics and econometrics methods.
  • Able and critical readers of academic articles with empirical contents

Learning outcomes

On completion of this unit successful students will be able to:

Knowledge and understanding

  • Explain the theoretical foundation of multiple regression analysis and effectively address common violations of the standard assumptions of the classical regression model.
  • Explain the techniques for econometric analysis of cross sectional data analysis.

Intellectual skills

  • Apply concepts in statistics and econometrics and critique statistical and econometric results presented in published articles and journals.
  • Employ concepts in statistics and econometrics to examine and analyse cross-section and data.

Practical skills

  • Produce empirical work, using alternative forms of regression analysis.
  • Analyse and interpret regression results.

Transferable skills and personal qualities

  • Use STATA software package for conducting multivariate regression analysis, interpreting results, and testing for violations of the assumptions of the classical linear regression model.
  • Examine published empirical work with statistical and econometric contents.
  • Apply skills acquired in the module in quantitative dissertation writing.

Employability skills

Analytical skills
Analyse data and interpret results for policy decision making.
Research
Collect relevant empirical data and organise them using appropriate tools.
Other
Develop skills that are relevant and needed in research institutes, financial institutions, Ministries, NGOs etc.

Assessment methods

Method Weight
Written exam 70%
Set exercise 30%

Feedback methods

Feedback on the assessments via VLE within SEED’s guidelines.

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Tutorials 10
Independent study hours
Independent study 110

Teaching staff

Staff member Role
Lawrence Ado-Kofie Unit coordinator

Return to course details

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