A Bayesian analysis of design parameters in survey data collectionShow others and affiliations
2018 (English)In: Journal of Survey Statistics and Methodology, ISSN 2325-0984, Vol. 6, no 4, p. 431-464Article in journal (Refereed) Published
Abstract [en]
In the design of surveys, a number of input parameters such as contact propensities, participation propensities, and costs per sample unit play a decisive role. In ongoing surveys, these survey design parameters are usually estimated from previous experience and updated gradually with new experience. In new surveys, these parameters are estimated from expert opinion and experience with similar surveys. Although survey institutes have fair expertise and experience, the postulation, estimation, and updating of survey design parameters is rarely done in a systematic way. This article presents a Bayesian framework to include and update prior knowledge and expert opinion about the parameters. This framework is set in the context of adaptive survey designs in which different population units may receive different treatment given quality and cost objectives. For this type of survey, the accuracy of design parameters becomes even more crucial to effective design decisions. The framework allows for a Bayesian analysis of the performance of a survey during data collection and in between waves of a survey. We demonstrate the utility of the Bayesian analysis using a simulation study based on the Dutch Health Survey.
Place, publisher, year, edition, pages
Oxford University Press, 2018. Vol. 6, no 4, p. 431-464
Keywords [en]
Adaptive survey design, Gibbs sampler, Nonresponse, Response propensities, Survey costs
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-76313DOI: 10.1093/jssam/smy012ISI: 000456506900001Scopus ID: 2-s2.0-85060913261OAI: oai:DiVA.org:oru-76313DiVA, id: diva2:1350975
Note
Funding Agency:
United Kingdom Leverhulme Trust International Network Grant IN-2014-046
2019-09-122019-09-122019-10-25Bibliographically approved