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Responsive Survey Design (RSD) aims to increase the efficiency of survey datacollection via live monitoring of paradata and the introduction of protocolchanges when survey errors and increased costs seem imminent. Daily predictionsof response propensity for all active sampled cases are among the mostimportant quantities for live monitoring of data collection outcomes, makingsound predictions of these propensities essential for the success of RSD.Because it relies on real-time updates of prior beliefs about key designquantities, such as predicted response propensities, RSD stands to benefit fromBayesian approaches. However, empirical evidence of the merits of theseapproaches is lacking in the literature, and the derivation of informativeprior distributions is required for these approaches to be effective. In thispaper, we evaluate the ability of two approaches to deriving priordistributions for the coefficients defining daily response propensity models toimprove predictions of daily response propensity in a real data collectionemploying RSD. The first approach involves analyses of historical data from thesame survey, and the second approach involves literature review. We find thatBayesian methods based on these two approaches result in higher-qualitypredictions of response propensity than more standard approaches ignoring priorinformation. This is especially true during the early-to-middle periods of datacollection when interventions are often considered in an RSD framework.

Deriving Priors for Bayesian Prediction of Daily Response Propensity in Responsive Survey Design: Historical Data Analysis vs. Literature Review