Research to understand and provide evidence for a generalised approach to data linkage

ObjectiveOrdinarily data linkage is approached on a project-by-project basis, tailoring the method to the research need. An increase in demand for data linkage has led to the development of a more efficient strategy, i.e. a generalised approach. Through data linkage, a series of indexes representing people, businesses, geography and classifications are created using administrative data. Additional data are linked to the indexes via a general method to collect a common identifier. Adopting a generalised approach requires an evidence base to ensure research aims can be met by the methods. This research will provide the evidence needed to underpin the strategy as well as guidance for implementing a generalised approach using an index. ApproachThe existing data linkage portfolio provides an opportunity to compare the application of a bespoke to a generalised methodology. Comparisons will collate information on: Index coverage; Linkage results and quality including precision and recall estimates; Analysis of the bias observed within the linked data. Of particular interest is the linkage of different population sub-groups, particularly those classified as vulnerable or disadvantaged, for example, data for prisoners, refugees, the homeless and third-sector organisations. ResultsWe will present the findings to date on recommendations for the use of a generalisable method for indexing, by exploring: Data linkage quality by bespoke methods versus a generalised approach The coverage strengths/limitations of the indexes Sub-groups representation in the linked data Conclusions/Implications Findings will provide evidence for adopting a new data linkage strategy, ensuring the most appropriate data linkage methods and quality assurance have been applied. Therefore, producing accurate and unbiased analysis to form the evidence base for policy decisions.

Authors

Publication Details

Journal
International Journal for Population Data Science
Published
2024-09-10
DOI
https://doi.org/10.23889/ijpds.v9i5.2561
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Research to understand and provide evidence for a generalised approach to data linkage

Rachel Huck, Sarah Cummins
International Journal for Population Data Science
Data Quality and Management
article

Research to understand and provide evidence for a generalised approach to data linkage

Rachel Huck, Sarah Cummins
article en

Abstract

ObjectiveOrdinarily data linkage is approached on a project-by-project basis, tailoring the method to the research need. An increase in demand for data linkage has led to the development of a more efficient strategy, i.e. a generalised approach. Through data linkage, a series of indexes representing people, businesses, geography and classifications are created using administrative data. Additional data are linked to the indexes via a general method to collect a common identifier. Adopting a generalised approach requires an evidence base to ensure research aims can be met by the methods. This research will provide the evidence needed to underpin the strategy as well as guidance for implementing a generalised approach using an index. ApproachThe existing data linkage portfolio provides an opportunity to compare the application of a bespoke to a generalised methodology. Comparisons will collate information on: Index coverage; Linkage results and quality including precision and recall estimates; Analysis of the bias observed within the linked data. Of particular interest is the linkage of different population sub-groups, particularly those classified as vulnerable or disadvantaged, for example, data for prisoners, refugees, the homeless and third-sector organisations. ResultsWe will present the findings to date on recommendations for the use of a generalisable method for indexing, by exploring: Data linkage quality by bespoke methods versus a generalised approach The coverage strengths/limitations of the indexes Sub-groups representation in the linked data Conclusions/Implications Findings will provide evidence for adopting a new data linkage strategy, ensuring the most appropriate data linkage methods and quality assurance have been applied. Therefore, producing accurate and unbiased analysis to form the evidence base for policy decisions.

International Journal for Population Data ScienceVol. 9(5)
Openalex Percentile: Top 82%
Data Quality and Management
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