Modelling time change in survey response rates: A Bayesian approach with an application to the Dutch Health Survey

Precise and unbiased estimates of response propensities (RPs) play a decisive role in the monitoring, analysis, and adaptation of data collection. In a fixed survey climate, those parameters are stable and their estimates ultimately converge when sufficient historic data is collected. In survey practice, however, response rates gradually vary in time. Understanding time-dependent variation in predicting response rates is key when adapting survey design. This paper illuminates time-dependent variation in response rates through multi-level time-series models. Reliable predictions can be generate... Mehr ...

Verfasser: Wu, Shiya
Boonstra, Harm-Jan
Moerbeek, Mirjam
Schouten, Barry
Dokumenttyp: Artikel
Erscheinungsdatum: 2023
Schlagwörter: Bayesian analysis / Multilevel model / Response propensity / Time series / Taverne
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29040131
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://dspace.library.uu.nl/handle/1874/434247

Precise and unbiased estimates of response propensities (RPs) play a decisive role in the monitoring, analysis, and adaptation of data collection. In a fixed survey climate, those parameters are stable and their estimates ultimately converge when sufficient historic data is collected. In survey practice, however, response rates gradually vary in time. Understanding time-dependent variation in predicting response rates is key when adapting survey design. This paper illuminates time-dependent variation in response rates through multi-level time-series models. Reliable predictions can be generated by learning from historic time series and updating with new data in a Bayesian framework. As an illustrative case study, we focus on Web response rates in the Dutch Health Survey from 2014 to 2019.