Q1: Suitable data sources

Statistics should be based on the most appropriate data to meet intended uses. The impact of any data limitations for use should be assessed, minimised and explained.

What you should commit to

Q1.1 Statistics should be based on data sources that are appropriate for the intended uses. The data sources should be based on definitions and concepts that are suitable approximations of what the statistics aim to measure, or that can be processed to become suitable for producing the statistics.

Q1.2 Statistics producers should establish and maintain constructive relationships with those involved in the collection, recording, supply, linking and quality assurance of data, wherever possible.

Q1.3 A clear statement of data requirements should be shared with the organisations that provide that data, setting out decisions on timing, definitions and format of data supply, and explaining how and why the data will be used.

Q1.4 Source data should be coherent across different levels of aggregation, consistent over time, and comparable between geographical areas, whenever possible.

Q1.5 The nature of data sources used, and how and why they were selected, should be explained. Potential bias, uncertainty and possible distortive effects in the source data should be identified and the extent of any impact on the statistics should be clearly reported.

Q1.6 The causes of limitations in data sources should be identified and addressed where possible. Statistics producers should be open about the extent to which limitations can be overcome and the impact on the statistics.

Q1.7 The impact of changes in the circumstances and context of a data source on the statistics over time should be evaluated. Reasons for any lack of consistency and related implications for use should be clearly explained to users.

Guidance and resources

Description Link Source
Guidance on quality assuring administrative data used to create statistics. It includes explanatory notes, case examples, Frequently Asked Questions, the actual toolkit (audit questionnaire), and questions to prompt thinking when conducting the audit. Quality Assurance of Administrative Data (QAAD) OSR
Guidance on quality assuring management information (MI) – aggregate information collated during the normal course of business to inform operational delivery, policy development or the management of performance. It includes examples of the practices to use. Quality Assurance of Management Information (QAMI) OSR
This guidance supports statistics producers in meeting the quality requirements of the UK Code of Practice for Statistics and understanding of Eurostat’s European Statistics Code of Practice, which sets out five dimensions for measuring the quality of statistical outputs. Quality statistics in government GSS
This strategy aims to improve statistical quality across the Government Statistical Service (GSS) to produce statistics that serve the public good. It sets out what the GSS should be doing to improve the quality of its statistics and manage the processes surrounding their production. GSS Quality Strategy GSS
These case studies provide examples of successful improvements to the quality of GSS statistics. GSS Quality Strategy case studies GSS