Despite its promising potential to reduce attrition and biases, the use of adaptive survey designs in panel studies is lacking in both areas that are needed for its functioning:

(1) In predicting nonresponse and thus creating appropriate strata as well as

(2) in the treatments that are administered in practice. This project will pair the implementation and testing of innovative prediction methodology from the field of machine learning with innovative treatments that can be assigned to likely nonrespondents. Prediction models will be trained and evaluated in a longitudinal framework that is tailored to identifying panelists at risk of nonparticipation in a given (new) panel wave. The predicted risk scores of the most accurate model allow us to test the effectiveness of different treatments. Specifically, this project will investigate the usage of innovative treatments in adaptive survey designs that aim to increase survey enjoyment compared to the more common differential incentives approach. Testing these strategies on a common ground will add to previous research on adaptive designs, which has been inconclusive about which approach works best for stimulating respondents’ participation and engagement. Furthermore, the treatments will not only be compared and evaluated with respect to their effects on participation, but also by being mindful about other, potential unintended, consequences on data quality in the long run. In addition, the transferability of the developed methodology to other panel studies will be investigated.