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 analyse response processes. One application is to predict sample members who are likely to be survey nonrespondents. The potential nonrespondents can then be targeted using responsive and adaptive strategies with the aim of incre...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
European Survey Research Association
2024-12-01
|
| Series: | Survey Research Methods |
| Subjects: | |
| Online Access: | https://ojs.ub.uni-konstanz.de/srm/article/view/8188 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823861433409994752 |
|---|---|
| author | Eliud Kibuchi Gabriele B Durrant Olga Maslovskaya Patrick Sturgis |
| author_facet | Eliud Kibuchi Gabriele B Durrant Olga Maslovskaya Patrick Sturgis |
| author_sort | Eliud Kibuchi |
| collection | DOAJ |
| description |
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 survey nonrespondents. The potential nonrespondents can then be targeted using responsive and adaptive strategies with the aim of increasing response rates and 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 data. In the Bayesian approach existing knowledge regarding model parameters is used to specify prior distributions. In this paper we apply this approach and analyse data from the UK household longitudinal study, Understanding Society (first five waves) and estimate informative priors from previous waves data. 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 leads to negligible improvement of the response propensity models’ predictive power and discriminative ability.
|
| format | Article |
| id | doaj-art-e2e72fd9c200446bba73d3a0156c62e5 |
| institution | Kabale University |
| issn | 1864-3361 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | European Survey Research Association |
| record_format | Article |
| series | Survey Research Methods |
| spelling | doaj-art-e2e72fd9c200446bba73d3a0156c62e52025-02-09T14:16:09ZengEuropean Survey Research AssociationSurvey Research Methods1864-33612024-12-01183An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal DataEliud Kibuchi0Gabriele B Durrant 1Olga Maslovskaya2Patrick Sturgis3University of GlasgowUniversity of Southampton University of Southampton The London School of Economic and Political Science 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 survey nonrespondents. The potential nonrespondents can then be targeted using responsive and adaptive strategies with the aim of increasing response rates and 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 data. In the Bayesian approach existing knowledge regarding model parameters is used to specify prior distributions. In this paper we apply this approach and analyse data from the UK household longitudinal study, Understanding Society (first five waves) and estimate informative priors from previous waves data. 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 leads to negligible improvement of the response propensity models’ predictive power and discriminative ability. https://ojs.ub.uni-konstanz.de/srm/article/view/8188response propensity modelsBayesianinformative priorsnonresponse |
| spellingShingle | Eliud Kibuchi Gabriele B Durrant Olga Maslovskaya Patrick Sturgis An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data Survey Research Methods response propensity models Bayesian informative priors nonresponse |
| title | An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data |
| title_full | An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data |
| title_fullStr | An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data |
| title_full_unstemmed | An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data |
| title_short | An Assessment of the Utility of a Bayesian Framework to Improve Response Propensity Models in Longitudinal Data |
| title_sort | assessment of the utility of a bayesian framework to improve response propensity models in longitudinal data |
| topic | response propensity models Bayesian informative priors nonresponse |
| url | https://ojs.ub.uni-konstanz.de/srm/article/view/8188 |
| work_keys_str_mv | AT eliudkibuchi anassessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT gabrielebdurrant anassessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT olgamaslovskaya anassessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT patricksturgis anassessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT eliudkibuchi assessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT gabrielebdurrant assessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT olgamaslovskaya assessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata AT patricksturgis assessmentoftheutilityofabayesianframeworktoimproveresponsepropensitymodelsinlongitudinaldata |