Quality Assurance of Administrative Data (QAAD) – Setting the standard

30 January 2015
Last updated:
15 December 2022

Administrative data

Administrative data refers to information collected primarily for administrative reasons (not research). This type of data is collected by government departments and other organisations for registration, transactions and record-keeping, usually when delivering a service. Administrative data are often used for operational purposes and their statistical use is secondary.

The Exposure Draft outlined some of the benefits and the challenges of using administrative data to produce official statistics. The purpose of this paper is to introduce the regulatory standard rather than set out a definitive description of those merits and risks.

The Authority recognises that there are limitations with administrative data and that these can create complications when compiling official statistics. However their use is central to the production of official statistics and the existence of these challenges places a premium on proactive quality assurance to investigate the data, manage identified issues, and clearly communicate any limitations to users. Quality Assurance of administrative data

Quality assurance of administrative data is more than simply checking that the figures add up. It is an ongoing, iterative process to assess the data’s fitness to serve their purpose. It covers the entire statistical production process and involves monitoring data quality over time and reporting on variations in that quality. Post-collection quality assurance methods, such as data validation, are an important part of the quality assurance process, but can be of limited value if the underlying data are of poor quality. The Authority encourages the application of critical judgment of the underlying data from administrative systems before the data are extracted for supply into the statistical production process. As with survey data, producers need to: investigate the administrative data to identify errors, uncertainty and potential bias in the data; make efforts to understand why these errors occur and to manage or, if possible, eliminate them; and communicate to users how these could affect the statistics and their use.

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