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THE ABS DATA QUALITY FRAMEWORK
In using this framework, it is important to understand that these six dimensions of quality are not independent of each other. The various elements of quality have a complex relationship and any action taken to address or modify one aspect of quality may affect one or more of the other elements. For example, all the other dimensions of quality impact on relevance and information provided to ensure statistics are interpretable will also serve to define coherence. Inevitably trade-offs must also be made between accuracy and timeliness, between continuity over time and revisions, between depth and completeness and response burden on data suppliers. Despite such dependencies and conflicts, the six dimensions can provide a useful basis for examining how quality should be managed within a statistical organisation. Achieving an acceptable level of quality is the result of addressing, managing, and balancing the various factors or elements that constitute better quality. Paying attention to the program objectives, the major uses of the data, costs and conditions that affect quality and user expectations is also important in determining an acceptable level of quality. The decision and actions that achieve this balance are based on knowledge, experience, reviews, user consultation and feedback, and judgement.