Quality measures for multisource statistics

Ton de Waal*, Arnout van Delden, Sander Scholtus

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
78 Downloads (Pure)


The ESSnet on Quality of Multisource Statistics is part of the ESS.VIP Admin Project. The main objectives of that latter project are (i) to improve the use of administrative data sources and (ii) to support the quality assurance of the output produced using administrative sources. The ultimate aim of the ESSnet is to produce quality guidelines for National Statistics Institutes (NSIs) that are specific enough to be used in statistical production at those NSIs. The guidelines aim to cover the diversity of situations in which NSIs work as well as restrictions on data availability. The guidelines will list a variety of potential measures, indicate for each of them their applicability and in what situation it is preferred or not, and provide an ample set of examples of specific cases and decision-making processes. Work Package 3 (WP 3) of the ESSnet focuses on developing and testing quantitative measures for measuring the quality of output based on multiple data sources and on methods to compute such measures. In particular, WP 3 focuses on non-sampling errors. Well-known examples of such quality measures are bias and variance of the estimated output. Methods for computing these and other quality measures often depend on the specific data sources. Therefore, we have identified several basic data configurations for the use of administrative data sources in combination with other sources, for which we propose, revise and test quantitative measures for the accuracy and coherence of the output. In this article we discuss the identified basic data configurations, the approach taken in WP 3, and give some examples of quality measures and methods to compute those measures. We also point out some topics for future work.
Original languageEnglish
Pages (from-to)179-192
JournalStatistical Journal of the IAOS
Issue number2
Publication statusPublished - 2019


  • administrative data
  • multi-source statistics
  • quality measures
  • survey data


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