We have initiated a large-scale probabilistic panel project in Catalonia, utilizing both web and paper survey modes. This approach allows us to systematically study nonresponse biases across various sociodemographic profiles, providing detailed insights into the factors influencing participation rates.
To improve response rates and mitigate nonresponse biases, we have conducted a series of recruitment experiments focusing on incentives and reminders. These experiments evaluate the effectiveness of diverse strategies, such as varying the type and amount of incentives offered and the frequency of reminders. Leveraging the data collected from these experiments, we have developed predictive models to estimate nonresponse probabilities based on participants’ characteristics and their previous responses in refreshment samples.
With access to an extensive dataset of nearly 90,000 cases, we utilize machine learning models to better understand and predict nonresponse behavior. Our focus extends beyond improving response rates; instead, we prioritize enhancing the representativity of the panel to achieve a more balanced and accurate sample. We also evaluate data quality to ensure that methodological innovations lead to more reliable results. Crucially, the Catalan Citizen Panel provides a unique opportunity to empirically test the effectiveness of Adaptive Survey Design (ASD), demonstrating how tailored protocols can optimize treatment allocation and improve samples for public opinion research.