PG Dip/MSc Social Statistics (Research Methods pathway)
Professional statisticians need to have a high degree of technical sophistication, which can only be gained through postgraduate study. In addition, they need to be able to listen to the needs of clients and communicate their findings to the user community. These skills are also developed within our postgraduate programmes.
This programme provides postgraduate instruction in the theory and methods of social statistics for students whose interests lie in the collection and analysis of quantitative and qualitative social science data, or demography.
You should apply using the University's online application form. More details are available on our Apply page.
Applications can be submitted at any time, although we would encourage applicants to apply before the end of May. If you are seeking financial support for your postgraduate studies, we recommend that you apply before the end of February so you have time to gather the necessary documentation for your funding body/sponsor.
If applying for one of our MSc programmes, please specify the optional modules you intend to take, if known.
For all applications, two academic references are required. Unfortunately, we cannot consider applications until we have received both references.
The programme is run by the Division of Social Statistics & Demography, with input from Mathematics and the Division of Sociology & Social Policy. Several modules are provided jointly for this programme and for the Diploma/MSc Statistics with Applications in Medicine (run by Mathematics).
There is a large choice of optional modules. If demography is where your interests lie, then, as well as the compulsory module in demographic methods, you can also take optional modules in more substantive areas. Epidemiological methods, survival analysis and modelling multilevel data are also relevant.
For those planning to analyse complex data, modelling courses such as modelling multilevel data and modelling longitudinal data are all relevant.
Other options include courses relevant to medical and social statistics.
The programme is normally full-time, and lasts for 12 months, with nine months of taught modules between October and June of the academic year, followed by three months of research and preparation of a masters dissertation. It is possible to take this programme over two years of part-time study, providing suitable arrangements can be made.
Since the Division of Social Statistics was founded in 1975, we have been at the forefront of international research into methodology for the design and analysis of sample surveys. Today, we are a leading international centre for research in social statistics.
Our broad range of research areas includes survey methods, sampling and estimation, analysing and modelling data, and non-response adjustments. These issues are central to the analysis of both physical and social science data.
Typical entry requirements
We welcome applications from students who have, or expect to have, a second-class honours degree.
Your application will be considered on its merits, with motivation and postgraduate experience in a statistical environment potentially important factors.
Non-graduate qualifications, in particular the Graduate Diploma of the Royal Statistical Society, may be accepted in place of an undergraduate degree. Full details of previous statistical training in the form of syllabuses, reading lists and, in particular, examination papers, are helpful in assessing your suitability for the programme.
This pathway is suitable for social scientists with some training in statistical methods, eg in geography, psychology, population sciences, sociology and politics. Degrees in statistics, mathematics, etc are also acceptable.
Typical course content
The year is divided into two semesters, each of 12 teaching weeks. In the week before the first semester begins, you attend a four-day induction course which introduces you to the computing and library facilities available at Southampton.
The programme of study consists of a combination of compulsory and option modules and a dissertation to be completed over the summer months. Each module is worth 10 or 20 CATS points, and a total of 120 points is required to complete the taught component of the programme.
Supervised research for MSc Social Statistics
If you pass the Diploma examinations, you will be permitted to undertake supervised research starting in June and to submit a dissertation for the MSc in September. Providing satisfactory supervision arrangements can be made, you can work on a topic of your own choice.
Dissertations are about 15,000 words in length. They should demonstrate your mastery of the topic area, but they are not expected to contain a substantial original contribution. They generally take the form of a computer-based analysis of social science data or a computer-based examination of a statistical technique.
- multilevel analysis of child survival in Lesotho
- the under-reporting of various crimes amongst different subgroups in the UK
- determinants of poverty in St Vincent and the Grenadines.
- volunteering and civic engagement
- child nutritional status in central Asia and eastern Europe
- whether family background impacts differently on educational achievement in comprehensive schools compared to traditionally tracked schools: a study on Germany with PISA data
- return migration in Jamaica
In addition to the compulsory modules listed below, option modules with a value of at least 30 CATS but no more than 35 CATS (ie two, three or four option modules) must be selected. If SOCI6041 is taken, options worth at least 20 CATS but no more than 25 CATS must be selected. Modules on other MSc programmes (eg MSc Economics) may be taken as options after discussion with your academic tutor and the MSc programme coordinator.
- Social Science Data: Sources and Measurement
- Research Skills
- Demographic Methods 1
- Qualitative Methods I
- Quantitative Methods I
- Survey Design
- Quantitative Methods IIA
- Analysis of Hierarchical (Multilevel & Longitudinal) Data
- Understanding Population Change
- Design of experiments
- Statistical computing (SAS, S-Plus)
- Computer Intensive Statistical Methods
- Multivariate Analysis
- Survey Methods I
- Population Projections
- Demographic Methods 2
- Population, Poverty and Policy
- Population and reproductive health
- Methods for Researching Ageing Societies
- Measurement errors
- Bayesian methods
- Epidemiological methods
Please note: This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if s/he takes full advantage of the learning opportunities that are provided. More detailed information can be found in the programme handbook (or other appropriate guide or website).
Learning and teaching
Modules are taught using a variety of methods, which may include lectures, seminars, group work or project work.
Lectures offer an overview of a topic, an explanation of difficult concepts or a discussion of key issues. Lectures presume a certain amount of additional reading, so it is often a good idea to read references before attending the corresponding lecture.
Seminars provide a forum for a closer examination of particular aspects of each module and are an important part of the learning process. Usually it is only by discussing and questioning aspects of a subject that their full implications can be understood. You will prepare papers and lead discussions or debates, and so develop your written and presentational skills.
The increasing use of web-based, video-based and PowerPoint-based teaching methods demonstrates our commitment to the effective use of available equipment and resources.
Each module will involve one or two sessions each week spread over the semester. For each module, students will be expected to write one or more essays (or equivalent), make presentations or contribute to seminar discussions.
Some modules are assessed by essays (or the equivalent), others by exams, and some by a mix of these methods. Exams are held at the end of each semester.