Prediction-based Adaptive Designs for Panel Surveys (PrADePS)
Abstract
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.
01.10.2022 – 30.09.2025
- MZES