PhD studentships are available for study in the Quantitative Methods cluster of the ESRC-funded Doctoral Training Centre. The Quantitative Methods cluster is a collaboration between the Department of Medical Statistics at the London School of Hygiene and Tropical Medicine (LSHTM), the Department of Quantitative Social Science and the Centre for Longitudinal Studies, both at the Institute of Education (IoE).
We seek applicants for postgraduate training in the application of quantitative methods to substantive issues in the health and social sciences and/or in the development and evaluation of statistical methods.
+3 studentships are available for PhD study starting in September 2014. Applicants will have a Masters degree in medical or social statistics, or equivalent qualifications or experience.
Type of applicant
Applicants should be interested in developing and applying quantitative methods in health and/or social science, with a background either in medical statistics or quantitative social science including (but not only) economics, geography, sociology, social policy and psychology or other quantitative backgrounds such as maths or statistics.
Advanced Training
As students progress, we anticipate they will need additional training to address methodological issues that arise and to understand how related methodologies complement each other. To meet this need, students will be able to draw on extensive advanced training expertise in both LSHTM and IoE to assemble a tailored programme of advanced study. Courses available include statistical analysis with missing data, statistical methods in epidemiology, longitudinal modelling, structural equation models, bootstrapping, and causal inference.
Possible Topics for PhD research
Examples of possible topics for PhD research include the following. In addition, we encourage students to come with their own proposals.
1. The design of efficient, practical, longitudinal studies.
2. Cluster randomised trials in education
3. The use of multiple imputation for missing data, in conjunction with sampling weights and other predictive estimands
4. Appropriate imputation strategies for missing data in complex social surveys, such as the British Household Panel Survey
5. Sensitivity analyses for non-response in social science
6. Statistical methods for understanding the effect of social networks
We welcome any topic from excellent candidates but are also especially interested in students who would like to research the following topic using advanced quantitative approaches:
Measurement error - Methods for handling exposure measurement error when there is a non-linear relationship between exposure and outcome, with a focus on using multiple imputation
Further information
Before applying for a scholarship, applicants are expected to contact either:
Dr Elizabeth Allen (email: [log in to unmask] Tel: +44 (0)20 7927 2943) OR
Dr Ruth Keogh (email: [log in to unmask]) for applicants interested in the specific measurement error proposal.
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