Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019
Significant data linkage work is taking place across government, as highlighted in a recent review by the Office for Statistics Regulation (“Joining up Data for Better Statistics”). This review provided insights on the scale of the data linkage work in government and its potential future opportuniti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Swansea University
2019-11-01
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| Series: | International Journal of Population Data Science |
| Online Access: | https://ijpds.org/article/view/1274 |
| _version_ | 1827613178426556416 |
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| author | Louise Palmer Charlie Wroth-Smith Anthony Edwards |
| author_facet | Louise Palmer Charlie Wroth-Smith Anthony Edwards |
| author_sort | Louise Palmer |
| collection | DOAJ |
| description | Significant data linkage work is taking place across government, as highlighted in a recent review by the Office for Statistics Regulation (“Joining up Data for Better Statistics”). This review provided insights on the scale of the data linkage work in government and its potential future opportunities. Based on this we are conducting a National Statistician’s Quality Review (NSQR) on data linkage.
These reviews investigate topics of current statistical interest and innovation with contributions from subject matter experts. The aims of this NSQR are to report on state-of-the-art data linkage methods and join up government work on data linkage to break down silos and share best practice.
We have scoped key topics of interest and linkage challenges by searching the literature and engaging with stakeholders across government and wider. Areas of interest include quality assurance of linkage, linking more than two data sources, linking encrypted data and challenges with data sharing and access for linking. Following this we approached recognised data linking experts e.g. academics, the private sector, the Office for National Statistics (ONS), wider Government Statistical Service (GSS) and other National Statistical Institutes (NSIs) to identify state-of-the-art linkage methods. This NSQR will outline the challenges faced across the GSS and set out the steps that should to be taken across the statistical system, building on the state-of-the-art data linkage methods set out by the experts.
We will present our progress and provisional findings on the leading methods being discussed in the review, as well as potential next steps for facilitating a more joined up way of working across the GSS to improve data linkage. |
| first_indexed | 2024-03-09T08:35:08Z |
| format | Article |
| id | doaj.art-9a3c79b9a65744d9a5b8aef910ea8e6e |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T08:35:08Z |
| publishDate | 2019-11-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-9a3c79b9a65744d9a5b8aef910ea8e6e2023-12-02T18:34:30ZengSwansea UniversityInternational Journal of Population Data Science2399-49082019-11-014310.23889/ijpds.v4i3.1274Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019Louise Palmer0Charlie Wroth-Smith1Anthony Edwards2Office for National StatisticsOffice for National StatisticsOffice for National StatisticsSignificant data linkage work is taking place across government, as highlighted in a recent review by the Office for Statistics Regulation (“Joining up Data for Better Statistics”). This review provided insights on the scale of the data linkage work in government and its potential future opportunities. Based on this we are conducting a National Statistician’s Quality Review (NSQR) on data linkage. These reviews investigate topics of current statistical interest and innovation with contributions from subject matter experts. The aims of this NSQR are to report on state-of-the-art data linkage methods and join up government work on data linkage to break down silos and share best practice. We have scoped key topics of interest and linkage challenges by searching the literature and engaging with stakeholders across government and wider. Areas of interest include quality assurance of linkage, linking more than two data sources, linking encrypted data and challenges with data sharing and access for linking. Following this we approached recognised data linking experts e.g. academics, the private sector, the Office for National Statistics (ONS), wider Government Statistical Service (GSS) and other National Statistical Institutes (NSIs) to identify state-of-the-art linkage methods. This NSQR will outline the challenges faced across the GSS and set out the steps that should to be taken across the statistical system, building on the state-of-the-art data linkage methods set out by the experts. We will present our progress and provisional findings on the leading methods being discussed in the review, as well as potential next steps for facilitating a more joined up way of working across the GSS to improve data linkage.https://ijpds.org/article/view/1274 |
| spellingShingle | Louise Palmer Charlie Wroth-Smith Anthony Edwards Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 International Journal of Population Data Science |
| title | Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 |
| title_full | Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 |
| title_fullStr | Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 |
| title_full_unstemmed | Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 |
| title_short | Improving Data Linkage in Government Statistics: The National Statistician’s Quality Review 2019 |
| title_sort | improving data linkage in government statistics the national statistician s quality review 2019 |
| url | https://ijpds.org/article/view/1274 |
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