A Bayesian analysis of design parameters in survey data collection

J.G. Schouten, N. Mushkudiani, Natalie Shlomo, Gabi Durrant, Peter Lundquist, James Wagner

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)431-464
Number of pages34
JournalJournal of Survey Statistics and Methodology
Issue number4
Publication statusPublished - 1 Dec 2018


  • Adaptive survey design
  • Gibbs sampler
  • Nonresponse
  • Response propensities
  • Survey costs


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