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Correlates of record linkage and estimating risks of non-linkage biases in business datasets

Correlates of record linkage and estimating risks of non-linkage biases in business datasets
Correlates of record linkage and estimating risks of non-linkage biases in business datasets
Researchers often utilise datasets that link information from multiple sources, but non-linkage biases caused by linked and non-linked subject differences are little understood, especially in business datasets. We address these knowledge gaps by studying biases in linkable 2010 UK Small Business Survey datasets. We identify correlates of business linkage propensity, and also for the first time its components: consent to linkage and register identifier appendability. As well, we take a novel approach to evaluating non-linkage bias risks, by computing dataset representativeness indicators (comparable, decomposable sample-subset similarity measures). We find that the main impacts on linkage propensities and bias risks are due to consenter / non-consenter differences explicable given business survey response processes, and differences between subjects with and without identifiers caused by register under-coverage of very small businesses. We then discuss consequences for the analysis of linked business datasets, and implications of the evaluation methods we introduce for linked dataset producers and users.
0964-1998
Moore, Jamie
5f015c47-3165-4f64-8561-7c047a9d2186
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Smith, Peter W F
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Moore, Jamie
5f015c47-3165-4f64-8561-7c047a9d2186
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Smith, Peter W F
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Moore, Jamie, Durrant, Gabriele and Smith, Peter W F (2017) Correlates of record linkage and estimating risks of non-linkage biases in business datasets. Journal of the Royal Statistical Society: Series A (Statistics in Society). (doi:10.1111/rssa.12342).

Record type: Article

Abstract

Researchers often utilise datasets that link information from multiple sources, but non-linkage biases caused by linked and non-linked subject differences are little understood, especially in business datasets. We address these knowledge gaps by studying biases in linkable 2010 UK Small Business Survey datasets. We identify correlates of business linkage propensity, and also for the first time its components: consent to linkage and register identifier appendability. As well, we take a novel approach to evaluating non-linkage bias risks, by computing dataset representativeness indicators (comparable, decomposable sample-subset similarity measures). We find that the main impacts on linkage propensities and bias risks are due to consenter / non-consenter differences explicable given business survey response processes, and differences between subjects with and without identifiers caused by register under-coverage of very small businesses. We then discuss consequences for the analysis of linked business datasets, and implications of the evaluation methods we introduce for linked dataset producers and users.

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Accepted/In Press date: 30 October 2017
e-pub ahead of print date: 6 December 2017

Identifiers

Local EPrints ID: 415232
URI: http://eprints.soton.ac.uk/id/eprint/415232
ISSN: 0964-1998
PURE UUID: 7c8bf6cd-ffa5-4fba-b1fb-2ebabd370fdd
ORCID for Peter W F Smith: ORCID iD orcid.org/0000-0003-4423-5410

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Date deposited: 03 Nov 2017 17:30
Last modified: 17 Dec 2019 05:49

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Contributors

Author: Jamie Moore
Author: Peter W F Smith ORCID iD

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