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Activity Number: 48 - Bayesian Adaptive Survey Designs
Type: Invited
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract #322334
Title: An assessment of the utility of a Bayesian framework to improve response propensity modelling
Author(s): Eliud Kibuchi* and Gabriele Durrant and Patrick Sturgis and Olga Maslovskaya
Companies: University of Southampton and University of Southampton and University of Southampton and University of Southampton
Keywords: Bayesian ; nonresponse ; predictive power ; response propensity models ;
Abstract:

Response propensity (RP) models are widely used in survey research to analyse response processes. One application is to predict sample members who are likely to be nonrespondents. The potential nonrespondents can then be targeted using responsive and adaptive strategies with aim of increasing response rates and reducing survey costs. Generally, however, RP models exhibit low predictive power, which limit their effective application in survey research to improve data collection processes. This paper explores whether the use of a Bayesian framework can improve the predictions of response propensity models. In a Bayesian approach, existing knowledge regarding model parameters is used to specify prior distributions. In this paper we apply the method in a longitudinal context and analyse data from the UK household longitudinal study (Understanding Society). We use estimates from RP models fitted to response outcomes from earlier waves as our source for specifying prior distributions. Our findings indicate that conditioning on previous wave data can lead to small improvements in the response propensity models' predictive power and discriminative ability.


Authors who are presenting talks have a * after their name.

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