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A Bayesian analysis of design parameters in survey data collection

A Bayesian analysis of design parameters in survey data collection
A Bayesian analysis of design parameters in survey data collection
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 on-going 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 a fair expertise and experience, the postulation, estimation and updating of survey design parameters is rarely done in a systematic way. This paper 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.
Bayesian Analysis , Adaptive and responsive survey designs
2325-0984
431-464
Schouten, Barry
d993fa0e-49d3-427e-a03e-f25263018f12
Mushkudiani, Nino
3518f791-319c-4cef-b63a-b574a7c3cb0a
Shlomo, Natalie
ebe478f7-d3e1-415a-af20-0d698ab74af6
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Lundquist, Peter
44d4a01c-f50e-405c-adf8-2513afb61c00
Wagner, James
2e4df4c8-9b9f-4006-a756-5cf05fece3f4
Schouten, Barry
d993fa0e-49d3-427e-a03e-f25263018f12
Mushkudiani, Nino
3518f791-319c-4cef-b63a-b574a7c3cb0a
Shlomo, Natalie
ebe478f7-d3e1-415a-af20-0d698ab74af6
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Lundquist, Peter
44d4a01c-f50e-405c-adf8-2513afb61c00
Wagner, James
2e4df4c8-9b9f-4006-a756-5cf05fece3f4

Schouten, Barry, Mushkudiani, Nino, Shlomo, Natalie, Durrant, Gabriele, Lundquist, Peter and Wagner, James (2018) A Bayesian analysis of design parameters in survey data collection. Journal of Survey Statistics and Methodology, 6 (4), 431-464. (doi:10.1093/jssam/smy012).

Record type: Article

Abstract

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 on-going 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 a fair expertise and experience, the postulation, estimation and updating of survey design parameters is rarely done in a systematic way. This paper 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.

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JSSAM-2017-029-R1_acceptedversion27042018 - Accepted Manuscript
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More information

Accepted/In Press date: 28 April 2018
e-pub ahead of print date: 11 July 2018
Published date: December 2018
Keywords: Bayesian Analysis , Adaptive and responsive survey designs

Identifiers

Local EPrints ID: 420182
URI: http://eprints.soton.ac.uk/id/eprint/420182
ISSN: 2325-0984
PURE UUID: 86fc132f-8a0e-4151-b9ff-e68225727784

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Date deposited: 01 May 2018 16:30
Last modified: 16 Mar 2024 06:33

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Contributors

Author: Barry Schouten
Author: Nino Mushkudiani
Author: Natalie Shlomo
Author: Peter Lundquist
Author: James Wagner

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