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Coordinated sampling: Theory, method and application at SFSO
Auteur(s)
Date de parution
2019-3-1
Mots-clés
Résumé
Starting in 2009, the Swiss Federal Statistical Office has been using a coordinated sampling procedure for its business surveys - and for its population and household surveys since 2010. The method, developed in Qualité (2009), is an extension of Brewer et al.’s (1972) procedure. It is based on the use of permanent random numbers and allows selecting, at each sampling occasion, a Poisson sample with chosen positive or negative coordination with respect to all past surveys. A priority needs to be assigned to these coordination requirements, as they may be inconsistent. By the end of 2018, 35 business surveys composed of 95 sub-samples were successfully selected using this system.
During this period, the sampling frame was updated twice-yearly using extracts of our business register. The pragmatic solution that we adopted consisted in transmitting history of ancient units to new units based on events recorded by register managers. However, an automatic treatment that matched completely the typology of demographic events of Eurostat (2010) was not possible: some information was missing in our records (e.g. in case of a Takeover, which business takes over which), and arbitration is needed for “n-to-m” events.
A challenging issue that arose with our population and household surveys is that of the sustainability of the coordinated sampling system. Indeed, the amount of necessary computations and stored data increase with the number of selected samples. While our tests showed that the business sampling system is able to accommodate hundreds more selections, a solution eventually has to be found for the very long run.
References:
Brewer, K., Early, L., and Joyce, S. (1972). Selecting several samples from a single population. Australian Journal of Statistics, 14 (3), 231-239.
Qualité, L. (2009). Unequal probability sampling and repeated surveys. PhD thesis, University of Neuchâtel.
During this period, the sampling frame was updated twice-yearly using extracts of our business register. The pragmatic solution that we adopted consisted in transmitting history of ancient units to new units based on events recorded by register managers. However, an automatic treatment that matched completely the typology of demographic events of Eurostat (2010) was not possible: some information was missing in our records (e.g. in case of a Takeover, which business takes over which), and arbitration is needed for “n-to-m” events.
A challenging issue that arose with our population and household surveys is that of the sustainability of the coordinated sampling system. Indeed, the amount of necessary computations and stored data increase with the number of selected samples. While our tests showed that the business sampling system is able to accommodate hundreds more selections, a solution eventually has to be found for the very long run.
References:
Brewer, K., Early, L., and Joyce, S. (1972). Selecting several samples from a single population. Australian Journal of Statistics, 14 (3), 231-239.
Qualité, L. (2009). Unequal probability sampling and repeated surveys. PhD thesis, University of Neuchâtel.
Notes
, ENBES workshop on Coordinated Sampling for Business Surveys, The Hague
Identifiants
Autre version
https://statswiki.unece.org/display/ENBES/ENBES+Workshop+2019+%28The+Hague%29+-+Workshop+on+Coordinated+Sampling
Type de publication
conference presentation
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