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.
Original language | English |
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Pages (from-to) | 637-662 |
Number of pages | 26 |
Journal | Journal of Official Statistics |
Volume | 38 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2022 |
Bibliographical note
Publisher Copyright:© 2022 Shiya Wu et al., published by Sciendo.
Keywords
- Bayesian
- expert elicitation
- Nonresponse bias
- response propensity