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International Journal of Social Research Methodology Volume 11, 2008 - Issue 1
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Articles

The Feasibility of linking household survey and administrative record data: New evidence for Britain

Stephen P. JenkinsInstitute for Social and Economic Research, University of Essex, ColchesterCO4 3SQ, UKCorrespondence[email protected]
,
Peter. LynnInstitute for Social and Economic Research, University of Essex, ColchesterCO4 3SQ, UK
,
Annette. JäckleInstitute for Social and Economic Research, University of Essex, ColchesterCO4 3SQ, UK;Economic and Social Research Council (ESRC)
&
Emanuela. SalaInstitute for Social and Economic Research, University of Essex, ColchesterCO4 3SQ, UK;Economic and Social Research Council (ESRC)
Pages 29-43 | Received 25 Feb 2006, Accepted 11 Apr 2007, Published online: 22 Jan 2008

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Emanuela Sala, Gundi Knies & Jonathan Burton. (2014) Propensity to consent to data linkage: experimental evidence on the role of three survey design features in a UK longitudinal panel. International Journal of Social Research Methodology 17:5, pages 455-473.
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Antonia Simon. (2014) Using administrative data for constructing sampling frames and replacing data collected through surveys. International Journal of Social Research Methodology 17:3, pages 185-196.
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Articles from other publishers (14)

Christopher Robert Bollinger & Iva Valentinova Tasseva. (2023) Income Source Confusion Using the SILC. Public Opinion Quarterly.
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Stephen P Jenkins & Fernando Rios-Avila. (2023) Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data. Journal of the Royal Statistical Society Series A: Statistics in Society 186:1, pages 110-136.
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Kien Nguyen Duc, Tiho Ancev & Alan Randall. (2021) Farmers' choices of climate-resilient strategies: Evidence from Vietnam. Journal of Cleaner Production 317, pages 128399.
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Stephen P. Jenkins & Fernando Rios Avila. (2021) Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data. SSRN Electronic Journal.
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Nancy Wang. (2019) Using address histories to improve the link rates of surveys in the Integrated Data Infrastructure (IDI). Statistical Journal of the IAOS 35:4, pages 577-585.
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Stefan Angel, Franziska Disslbacher, Stefan Humer & Matthias Schnetzer. (2019) What did you Really Earn Last Year?: Explaining Measurement Error in Survey Income Data. Journal of the Royal Statistical Society Series A: Statistics in Society 182:4, pages 1411-1437.
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P. Dolan & M. M. Galizzi. (2015) Getting policy-makers to listen to field experiments. Oxford Review of Economic Policy 30:4, pages 725-752.
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Lisa Crosato, Mauro Mussini, Paolo Mariani & Biancamaria Zavanella. 2014. Statistical Methods and Applications from a Historical Perspective. Statistical Methods and Applications from a Historical Perspective 153 162 .
Emanuela Sala. (2013) Le indagini campionarie del nuovo millennioThe surveys of the new millennium. What’s new?. Quaderni di Sociologia:62, pages 77-90.
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Gillian Harper & Les Mayhew. (2011) Using Administrative Data to Count Local Populations. Applied Spatial Analysis and Policy 5:2, pages 97-122.
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Francesco Figari, Maria Iacovou, Alexandra J. Skew & Holly Sutherland. (2011) Approximations to the Truth: Comparing Survey and Microsimulation Approaches to Measuring Income for Social Indicators. Social Indicators Research 105:3, pages 387-407.
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Peter Lynn, Annette Jäckle, Stephen P. Jenkins & Emanuela Sala. (2012) The Impact of Questioning Method on Measurement Error in Panel Survey Measures of Benefit Receipt: Evidence from a Validation Study. Journal of the Royal Statistical Society Series A: Statistics in Society 175:1, pages 289-308.
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Tom W. Smith. (2011) The Report of the International Workshop on Using Multi-level Data from Sample Frames, Auxiliary Databases, Paradata and Related Sources to Detect and Adjust for Nonresponse Bias in Surveys*. International Journal of Public Opinion Research 23:3, pages 389-402.
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Emanuela Sala & Peter Lynn. (2007) The potential of a multi-mode data collection design to reduce non response bias. The case of a survey of employers. Quality & Quantity 43:1, pages 123-136.
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