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Large-scale, cross-sectional government datasets; research published and recent developments

Authors: Jo Wathan; Vanessa Higgins;

Large-scale, cross-sectional government datasets; research published and recent developments

Abstract

The United Kingdom is fortunate in having a plethora of microdata datasets available for use by the academic community. Major cross-sectional datasets collected on behalf of central government and are routinely made available to secondary analysis via the UK Data Archive at the University of Essex, supported by the Government surveys group of the Economic and Social Data Service (ESDS Government) led by the Cathie Marsh Centre for Census and Survey Research at the University of Manchester. The data include a wide range of specialist surveys collected principally reasons of policy development and monitoring. The surveys are continuous, large-scale and cross-sectional. The portfolio includes surveys such as the General Household Survey, Labour Force Survey, British Crime Survey, Health Survey for England, Expediture and Food Survey, Family Resources Survey, British Social Attitudes Survey and national variants. Many of these have surveys have been running for decades providing considerable scope for assessing social change. This paper reviews the way in which these datasets are currently being used by the UK academic community and highlights the research potential offered by these important and highly flexible resources. The paper will also explain the way in which recent and future developments in dissemination and value added materials for users enable facilitate increased use of the data. Further information about surveys supported by ESDS Government can be found online at http://www.esds.ac.uk/government

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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