Abstract
Creating statistics by combining data sources allows for the production of new, more timely and/or more detailed statistics. With an intended statistical output in mind, and various potentially useful data sources, there is a need to assess the potential of each source to contribute to the intended statistic. Quality frameworks provide tools for such tasks. This paper proposes a quality framework that includes dimensions applicable to survey, administrative and big data to support the assessment of the potential of each source to contribute to the intended statistic. The framework is applied to a case study of mobility data and a case study of virus particle detection in sewage data.
| Original language | English |
|---|---|
| Pages (from-to) | 439-446 |
| Number of pages | 8 |
| Journal | Statistical Journal of the IAOS |
| Volume | 39 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 30 May 2023 |
Keywords
- Data integration
- Selectivity
- Derivation variables
- Validation
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