Advanced Quantitative MethodsFind a programme
|Run by||Faculty of Social Sciences and Law|
Four years full-time;
seven years part-time
|Part-time study available||Yes|
|Open to international students||Yes|
|Number of places||Not fixed|
|Start date||September 2016|
The Advanced Quantitative Methods (AQM) pathway is for social scientists who wish to learn advanced quantitative methods for secondary-data analysis, and apply these methods appropriately to answer particular substantive questions from their disciplines. This includes social scientists interested in inter-disciplinary research involving the application of quantitative methods from one discipline to problems in another.
The AQM pathway is also for statistically trained researchers whose interests are more methodological. Projects may involve applying statistical methods used in other disciplines to social science problems, or developing novel statistical methods for analysing social-science data. We welcome applications from students with backgrounds in statistics or related disciplines.
Fees for 2016/17
Full time fees
Part time fees
Fees quoted are provisional, per annum and subject to annual increase.
Funding for 2016/17
The Faculty of Social Sciences and Law has an allocation of 1+3 and +3 ESRC scholarships. Applicants may also be interested in applying for funding from the University of Bristol Scholarships and Alumni PhD Scholarships.
Further information on funding for prospective UK, EU and international postgraduate students.
A Masters (or equivalent) in a discipline from the social or health sciences, and familiarity with intermediate-level quantitative techniques or multiple regression. For more methodological projects within the areas of social statistics and biostatistics, an MSc in Statistics or equivalent (eg Econometrics) is required.
See international equivalent qualifications on the International Office website.
|Selection process||Online application form|
|English language requirements||
Further information about English language requirements
|Admissions statement||Read the programme admissions statement for important information on entry requirements, the application process and supporting documents required.|
Not fixed but early application is advised. Deadlines for funded applications are likely to be in February.
The ESRC has designated AQM as a priority area, and the purpose of the pathway is to provide training and supervision that emphasizes quantitative methods to a higher level than is usually offered. Training includes a structured programme of core AQM courses and a fortnightly research and reading group.
Details of the core training currently offered can be found on the AQM courses website.
Key research interests
You will need two supervisors: one supervisor with AQM expertise and one supervisor with expertise in your chosen substantive area. Your main supervisor must be from a social science discipline.
The following AQM course leaders are all potential supervisors with AQM expertise:
- Dr Rich Harris
- Dr. Malcolm Fairbrother
- Professor Kelvyn Jones
- Dr George Leckie
- Dr David Manley
- Professor Clive Sabel
- Professor Sarah Smith
- Professor Kate Tilling
- Dr. Liz Washbrook
- Professor Frank Windmeijer
- Professor William Browne
For other potential supervisors, please consult our academic staff pages on the relevant school websites.
The PhD in Advanced Quantitative Methods offers preparation for a wide range of careers. Our students go on to employment in a wide variety of areas frequently where statistical analysis is required, but also where numeracy is crucial. AQM students also continue within academic careers, going to post-doctoral positions and lecturing positions.
At its core, the programme establishes a sound research training with a set of bespoke advanced training courses to provide students with the advanced quantitative toolkit required for their PhD research along with practical applications to position our students to be competitive for different types of employment including research, policy and intervention implementation.
Dr Malcolm Fairbrother, (Senior Lecturer, Research Fellow), Macro-social determinants of individual level outcomes; relationships between political economy and the natural environment; social change.
Dr Rich Harris, (Professor), Methodological interests in spatial statistics, spatial econometrics, GIS and geodemographics; spatial measures of inequality and segregation; the geographies of education under systems of choice.
Professor Kelvyn Jones, (Professor), Geography of health: geographical inequalities in mortality in advanced economies; realistically complex modelling: the quantitative analysis of social science data; research design: developing evidence-based research in non-experimental studies.
Dr George Leckie, (Senior Lecturer), Longitudinal data analysis and dyadic data analysis; multilevel analysis of non-hierarchical data; the application and dissemination of multilevel and other latent variable models to analyse educational and social science data.
Dr David Manley, (Senior Lecturer), Segregation: multiple approaches to measuring and analysing; urban geography: urban inequalities, integration of the life course into static models.
Professor Clive Sabel, (Chair in Quantitative Geography), Environmental and social exposure; health geography; social deprivation and inequalities.
Professor Sarah Smith, (Professor, Head of Department), Analysis of the effects of policy reform; intergenerational transmission; modelling individual behaviour, including fertility, health, retirement, charitable giving and subjective well-being.
Professor Kate Tilling, (Professor), Childhood growth; complex interrelationships which change over time (eg. fat mass and physical activity, risk factors for CHD); lifecourse analyses; trajectories of change in disease outcomes.
Dr Liz Washbrook, (Lecturer), Relationships between family background, policy and early childhood outcomes and methodological issues in the analysis of longitudinal data.
Professor Frank Windmeijer, (Professor), Demand for health and health care; policy evaluation; using genetic markers in social science research.
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