Data Collection Expert Prior Elicitation in Survey Design: Two Case Studies

Wu Shiya (), Schouten Barry (), Meijers Ralph () and Moerbeek Mirjam ()
Additional contact information
Wu Shiya: Department of Methodology and Statistics, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands.
Schouten Barry: Department of Process Development and Methodology, Statistics Netherlands, P.O. Box 24500, 2490 HA Den Haag, The Netherlands.
Meijers Ralph: Department of Traffic and Transport of Division Social Statistics, Statistics Netherlands, P.O. Box 4481, 6401 CZ Heerlen, The Netherlands.
Moerbeek Mirjam: Department of Methodology and Statistics, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands.

Journal of Official Statistics, 2022, vol. 38, issue 2, 637-662

Abstract: Data collection staff involved in sampling designs, monitoring and analysis of surveys often have a good sense of the response rate that can be expected in a survey, even when this survey is new or done at a relatively low frequency. They make expectations of response rates, and, subsequently, costs on an almost continuous basis. Rarely, however, are these expectations formally structured. Furthermore, the expectations usually are point estimates without any assessment of precision or uncertainty. In recent years, the interest in adaptive survey designs has increased. These designs lean heavily on accurate estimates of response rates and costs. In order to account for inaccurate estimates, a Bayesian analysis of survey design parameters is very sensible. The combination of strong intrinsic knowledge of data collection staff and a Bayesian analysis is a natural next step. In this article, prior elicitation is developed for design parameters with the help of data collection staff. The elicitation is applied to two case studies in which surveys underwent a major redesign and direct historic survey data was unavailable.

Keywords: Nonresponse bias; Bayesian; response propensity; expert elicitation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:38:y:2022:i:2:p:637-662:n:4

DOI: 10.2478/jos-2022-0028

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