Master of Science
MSc Social Research Methods and Statistics
Training in advanced quantitative methods at a university at the forefront of social statistics research.
Due to high demand for this course, we operate a staged admissions process with multiple selection deadlines throughout the year, to maintain a fair and transparent approach.
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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): £15,800
International, including EU, students (per annum): £32,600
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.
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.
- Learn more about - student finance options for UK students .
- Learn more about - fees and finance for international students .
- Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.
Course unit details:
Statistical Foundations
| Unit code | SOST70151 |
|---|---|
| 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 | Social Statistics |
| Available as a free choice unit? | Yes |
Overview
To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions.
Aims
To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions.
Learning outcomes
On successful completion of this course unit, students will
• Understand and do calculus involving fundamental concepts in probability theory such as independence and conditional probabilities
• Have working handle on random variables and their properties
• Have a basic understanding of estimators and how these relate to a model for data
• Being able to perform basic tests of hypothesis and being able to generalise this understanding beyond the standard cases
• Critically assess the extent to which a statistical analysis meets required assumptions
• Have a broad knowledge general issues in statistical inference
Teaching and learning methods
Twelve teaching occasions comprising a lecture component and a practical. The practical element may involve computer based activities and/or discussion sessions. Computer exercises will be done using the R environment and will not be scheduled every week. A number of extra tutorials led by the course TAs will be scheduled in addition.
Assessment methods
Weekly tests (x8, 30% total)
Exam (70%)
Feedback methods
Feedback available via Turnitin
Recommended reading
Preliminary main reading
• Agresti, A. (2018) Statistical Methods for the Social Sciences (5th Edition). Pearson International Edition.
Online learning modules on R
• https://www.datacamp.com/swirl-r-tutorial
• http://eclr.humanities.manchester.ac.uk/index.php/R
Additional readings may include excerpts from
• Bluman, A. G. (2012). Probability demystified.
• McGraw-Hill.Gill, J. (2006)Essential Mathematics for Political and Social Research. Cambridge University Press. (Electronic version available in UoM library)
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 20 |
| Tutorials | 8 |
| Independent study hours | |
|---|---|
| Independent study | 122 |
Teaching staff
| Staff member | Role |
|---|---|
| Philip Leifeld | Unit coordinator |
Additional notes
Information
Compulsory for SRMS
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