<abstract xmlns="http://www.w3.org/1999/xhtml">

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.
</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.

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

Department of Process Development and Methodology, Statistics Netherlands

Department of Traffic and Transport of Division Social Statistics, Statistics Netherlands

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Data Collection Expert Prior Elicitation in Survey Design: Two Case Studies"}

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...

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

Data publikacji: 14 cze 2022

Zakres stron: 637 - 662

Otrzymano: 01 mar 2021

Przyjęty: 01 sty 2022

DOI: https://doi.org/10.2478/jos-2022-0028

Słowa kluczowe
Nonresponse bias, Bayesian, response propensity, expert elicitation

© 2022 Shiya Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Data publikacji: 14 cze 2022

Zakres stron: 637 - 662

Otrzymano: 01 mar 2021

Przyjęty: 01 sty 2022

DOI: https://doi.org/10.2478/jos-2022-0028

Słowa kluczoweNonresponse bias, Bayesian, response propensity, expert elicitation

© 2022 Shiya Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Nonresponse bias, Bayesian, response propensity, expert elicitation