Prediction-based Adaptive Designs for Panel Surveys (PrADePS)

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.