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PhD Studentship (ESRC South Coast DTP) - A framework for estimating the quality of official statistics from administrative data

Social Statistics & Demography

Location:  Highfield Campus
Salary:   £14553
Annual stipend of £14553 plus payment of programme fees.
Closing Date:   Wednesday 28 June 2017
Reference:  878817CC

A framework for estimating the quality of official statistics from administrative data

A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP)

Supervisory Team:

Prof. Peter Smith (Division of Social Statistics and Demography)( and colleagues from S3RI & Office for National Statistics (ONS)

The aim of this research is to develop a new framework for estimation in official statistics based on administrative data as we move from the current one based on census and probability surveys. More specifically, the project’s objectives are to: a) Develop methods for describing and measuring the quality of estimates based on administrative data sources; and b) Research optimal designs for data collections such as surveys and experiments for use with administrative data in order to make valid statistical inferences about the estimates (for example designed to adjust for coverage and measurement error in the administrative data).

Research questions:

1) How can we describe, and measure the quality of, estimates based on administrative data?
2) How can the collection of administrative data be improved in order to draw reliable estimates?

Many of the key official statistics in the UK are derived from probability surveys and the population census - with administrative data often used in the design and processing stages as auxiliary information in order to improve the precision of the estimates. With the increased availability of administrative data and the decline in response rates to surveys, the use of census and surveys is expected to decline with administrative data providing the primary basis for estimation. Thus, official statistics in the UK are changing from being based on ‘designed’ data collections such as Census and Surveys to being based on ‘found’ data collections such as administrative data. This is reflected in the UK Statistics Authority’s Strategy for official statistics:  Better Statistics, Better Decisions as well within ONS on initiatives such as the Administrative Data Census and the support for the Administrative Data Research Network. Against this background, the aim of this PhD is to contribute to the development of a statistical methodology to create estimates from administrative data, focusing on how to measure the quality of these estimates. While it is relatively straight forward to obtain an estimate from administrative data, it is challenging to provide a well-calibrated measure of uncertainty which takes into account all of the potential errors. There is no sampling theory on which to base the estimation of standard errors. It is likely that Bayesian methods will be developed since they provide a natural framework for incorporating uncertainty including information from experts, as well as other methods such as capture-recapture methods to adjust for under-coverage. The PhD will also involve designing experiments and surveys to assess the quality of administrative data.

Skills required of the PhD student
• Essential: Background in mathematics and/or statistics with an understanding of surveys.
• Essential: Good understanding of statistical theory
• Desirable: Knowledge of design and model based estimation.


South Coast DTP Funding provides an annual maintenance grant (tax free) of £14553, plus payment of all programme fees.  Other funding available for SCDTP funded students can be found on the SCDTP website (

Funding is provided for 3 years full-time PhD study (pro-rata for part-time students).  Applications for 1+3 funding for students completing a Master's year prior to the commencement of PhD study are also welcome (details available at

Application Procedure

The closing date and time for applications is noon on 28th June 2017.  The full application procedure, the funding application form, and more information on the South Coast Doctoral Training Partnership can be found at:

For further information about this project, please contact the lead supervisor detailsed above.  For questions relating to the application procedure, or for more information about the SCDTP, please visit the SCDTP website or contact us at

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position



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