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Multi‐source Statistics: Basic Situations and Methods

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  • Ton de Waal
  • Arnout van Delden
  • Sander Scholtus

Abstract

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems.

Suggested Citation

  • Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:1:p:203-228
    DOI: 10.1111/insr.12352
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    References listed on IDEAS

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    2. Szymkowiak Marcin & Wilak Kamil, 2021. "Repeated weighting in mixed-mode censuses," Economics and Business Review, Sciendo, vol. 7(1), pages 26-46, March.
    3. Roberta Varriale & Marco Alfo’, 2023. "Multi-source statistics on employment status in Italy, a machine learning approach," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 37-63, April.
    4. Rocci Fabiana & Varriale Roberta & Luzi Orietta, 2022. "Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes," Journal of Official Statistics, Sciendo, vol. 38(2), pages 533-556, June.

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