An assessment of the utility of a Bayesian framework to improve response propensity models in longitudinal data
An assessment of the utility of a Bayesian framework to improve response propensity models in longitudinal data
Response propensity (RP) models are widely used in survey research to assist in targeting fieldwork resources. With a good RP model, likely nonrespondents can be targeted using responsive and adaptive strategies with the aim of increasing response rates, improving survey efficiency, and potentially reducing survey costs. Generally, however, RP models exhibit low predictive power, which limits their effective application in survey research to improve data collection. This paper explores whether the use of a Bayesian framework can improve the predictions of response propensity models in longitudinal contexts. In this approach coefficients from a previous wave RP model are used to specify informative priors for the next wave model. We apply this approach using the first five waves of the Understanding Society panel survey. Our findings indicate that conditioning on previous wave data does not produce worthwhile improvements in the predictive accuracy of the response propensity models.
Kibuchi, Eliud
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Durrant, Gabriele B.
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Maslovskaya, Olga
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Sturgis, Patrick
b9f6b40c-50d2-4117-805a-577b501d0b3c
Kibuchi, Eliud
a8e48182-8b0a-48f9-8c53-f610726b0974
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Maslovskaya, Olga
9c979052-e9d7-4400-a657-38f1f9cd74d0
Sturgis, Patrick
b9f6b40c-50d2-4117-805a-577b501d0b3c
Kibuchi, Eliud, Durrant, Gabriele B., Maslovskaya, Olga and Sturgis, Patrick
(2024)
An assessment of the utility of a Bayesian framework to improve response propensity models in longitudinal data.
Survey Research Methods.
(In Press)
Abstract
Response propensity (RP) models are widely used in survey research to assist in targeting fieldwork resources. With a good RP model, likely nonrespondents can be targeted using responsive and adaptive strategies with the aim of increasing response rates, improving survey efficiency, and potentially reducing survey costs. Generally, however, RP models exhibit low predictive power, which limits their effective application in survey research to improve data collection. This paper explores whether the use of a Bayesian framework can improve the predictions of response propensity models in longitudinal contexts. In this approach coefficients from a previous wave RP model are used to specify informative priors for the next wave model. We apply this approach using the first five waves of the Understanding Society panel survey. Our findings indicate that conditioning on previous wave data does not produce worthwhile improvements in the predictive accuracy of the response propensity models.
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Accepted/In Press date: 20 June 2024
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Local EPrints ID: 492202
URI: http://eprints.soton.ac.uk/id/eprint/492202
PURE UUID: 6b68f6b6-1d4a-42df-8809-1fb3d2ef9f27
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Date deposited: 22 Jul 2024 16:34
Last modified: 03 Jul 2025 01:47
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Author:
Eliud Kibuchi
Author:
Patrick Sturgis
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