  Responsive Survey Design (RSD) aims to increase the efficiency of survey data
collection via live monitoring of paradata and the introduction of protocol
changes when survey errors and increased costs seem imminent. Daily predictions
of response propensity for all active sampled cases are among the most
important quantities for live monitoring of data collection outcomes, making
sound predictions of these propensities essential for the success of RSD.
Because it relies on real-time updates of prior beliefs about key design
quantities, such as predicted response propensities, RSD stands to benefit from
Bayesian approaches. However, empirical evidence of the merits of these
approaches is lacking in the literature, and the derivation of informative
prior distributions is required for these approaches to be effective. In this
paper, we evaluate the ability of two approaches to deriving prior
distributions for the coefficients defining daily response propensity models to
improve predictions of daily response propensity in a real data collection
employing RSD. The first approach involves analyses of historical data from the
same survey, and the second approach involves literature review. We find that
Bayesian methods based on these two approaches result in higher-quality
predictions of response propensity than more standard approaches ignoring prior
information. This is especially true during the early-to-middle periods of data
collection when interventions are often considered in an RSD framework.
