Quality Assurance of Administrative Data (QAAD)
This publication was updated in December 2022 to migrate the contents to HTML and improve accessibility.
The Office for Statistics Regulation’s Regulatory Standard for the quality assurance of administrative data that are used to produce official statistics is presented below. This comprises:
- Quality Assurance of Administrative Data – Setting the Standard
- Administrative Data Quality Assurance Toolkit (February 2019)
- Quality Assurance of Management Information (QAMI) Guidance
The Standard recognises the increasing role that administrative data are playing in the production of official statistics and clarifies our expectations for what producers of official statistics should do to assure themselves of the quality of these data.
The toolkit that supports it provides helpful guidance to statistical producers about the practices they can adopt to assure the quality of the data they receive, and sets out the standards that our Assessors will use when assessing statistics against the Code of Practice for Official Statistics.
We have produced a range of additional guidance documents that help explain our approach to the quality assurance of data – please see the related links panel:
- The Frequently Asked Questions document answers questions raised with us by statistical producers within the UK
- QAAD Questions sets out a range of questions that producers can ask when investigating their data sources
- QA of Management Information (QAMI) provides guidance for producers of management information based on the QAAD approach
- Case example – Department for Communities and Local Government’s Indices of Deprivation illustrates basic and enhanced quality assurance of a mix of administrative data sources
Over the summer of 2016 we undertook a review to listen to and learn from the experience of using QAAD for producers of statistics (and for our regulatory team). We have published our QAAD evaluation report. It sets out our findings and next steps as we continue to promote the quality assurance of data.