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Research project

Administrative Data Research Centre for England

Project overview

The Administrative Data Research Centre for England (ADRC-E) was led by the University of Southampton, in partnership with colleagues from the Bloomsbury group of the University of London and the Office for National Statistics. It was funded by the Economic and Social Research Council as part of the Administrative Data Research Network (ADRN). The centre had a distinctive interdisciplinary research programme, focused on methods development and policy-relevant research using linked administrative data from government departments. The centre ran an extensive training programme for researchers and hosted secure access facilities in Southampton, London and Titchfield.

Staff

Lead researcher

Professor Peter Smith

Professor in Social Statistics
Other researchers

Professor Gabriele Durrant PhD

Prof of Social Stats & Survey Method

Research interests

  • survey data collection
  • survey methodology
  • statistical modelling

Professor Graham Moon PhD, FAcSS, HonMFPH

Research interests

  • Place effects on health-related behaviour, particularly smoking, diet and alcohol consumption
  • Applications of multilevel modelling in human geography; small area estimation methods
  • Post-asylum geographies and mental health care delivery

Dr Ben Anderson

Principal Research Fellow

Research interests

  • ‘big’ data-driven analysis of habitual energy and water consumption
  • the impacts of interventions or natural socio-technical change on patterns of demand
  • the linkage of 'appliance' monitoring with traditional forms of social science data in support of socio-technical innovation

Professor Li-Chun Zhang

Professor in Social Statistics

Research interests

  • Graph sampling
  • Analysis of integrated data
  • Statistical uses of administrative sources

Collaborating research institutes, centres and groups

Population, Health and Wellbeing (PHeW)

We combine expertise in population, health and wellbeing with the application of technical excellence in geographic information systems (GIS), spatial analysis and qualitative methods.

Research outputs

Do coefficients of variation of response propensities approximate non-response biases during survey data collection?
Jamie C. Moore,
Gabriele B. Durrant,
& Peter W.F. Smith
, 2021 , Journal of the Royal Statistical Society: Series A (Statistics in Society) , 184 (1) , 301--323
DOI: 10.1111/rssa.12624
Type: article
Health and social care diversity among individuals with longstanding physical and psychological health problems
Corine Driessens,
David Kingdon,
David Pilgrim,
& Peter W.F. Smith
, 2020 , Community Mental Health Journal , 56 (5) , 978--987
DOI: 10.1007/s10597-020-00566-y
Type: article
Antibiotic usage in chronic rhinosinusitis: analysis of national primary care electronic health records
Claire Hopkins,
Elizabeth Williamson,
Steve Morris,
Caroline S. Clarke,
Mike Thomas,
Paul Little,
Valerie J. Lund,
Helen Blackshaw,
Anne G.M. Schilder,
Carl Philpot,
& Spiros Denaxas
, 2019 , Rhinology
DOI: 10.4193/Rhin19.136
Type: article
Clarithromycin and endoscopic sinus surgery for adults with chronic rhinosinusitis with and without nasal polyps
Carl Philpott,
Steffi Le Conte,
David Beard,
Jonathan Cook,
William Sones,
Steve Morris,
Caroline S. Clarke,
Mike Thomas,
Paul Little,
Jane Vennik,
Valerie Lund,
Helen Blackshaw,
Anne Schilder,
Stephen Durham,
Spiros Denaxas,
James Carpenter,
James Boardman,
Claire Hopkins,
Neil Med,
Keith Boland,
Jane Woods,
& Teresa Ferreira
, 2019 , Trials , 20 (1)
DOI: 10.1186/s13063-019-3314-7
Type: article
Expert panel process to optimise the design of a randomised controlled trial in chronic rhinosinusitis (the MACRO programme)
Helen Blackshaw,
Jane Vennik,
Carl Philpott,
Mike Thomas,
Caroline Eyles,
James Carpenter,
Caroline S. Clarke,
Stephen Morris,
Anne G.M. Schilder,
Valerie Lund,
Paul Little,
Stephen Durham,
Spiros Denaxas,
Elizabeth Williamson,
David Beard,
Jonathan Cook,
Steffi Le Conte,
K Airey,
J Boardman,
& Claire Hopkins
, 2019 , Trials , 20
DOI: 10.1186/s13063-019-3318-3
Type: article
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