Adaptive and responsive survey designs rely on estimates of survey data collection parameters (SDCPs), such as response propensity, to make intervention decisions during data collection. These interventions are made with some data collection goal in mind, such as maximizing data quality for a fixed cost or minimizing costs for a fixed measure of data quality. As a result, the quality of the estimates of the SDCPs influences which cases are identified for interventions and the ultimate effectiveness of intervention decisions. Higher quality estimates of SDCPs lead to more optimal intervention decisions, allowing survey managers to betrer balance quality goals with external constraints, such as cost.

This talk discusses an experiment where a Bayesian framework was employed to improve estimates of SDCPs during data collection, which were then used in real-time simulations to identify cases for intervention. The use of Bayesian methods introduced modest improvements in the predictions of SDCPs, especially early in data collection, when interventions would have the largest effect on survey outcomes. Additionally, the experiment resulted in significant data collection cost savings without having a significant effect on a key survey estimate. While there are many areas for future research in this area, this experiment suggests that Bayesian methods can improve predictions of SDCPs that are critical for adaptive and responsive data collection interventions.