Odum Institute: Utilizing Probability Panels
PLEASE NOTE: This class will be covered over two afternoons: 10/23/2023 from 2pm - 4pm AND 10/25/2023 from 3pm - 5pm
Due to increasing survey costs and declining response rates, probability panels have become a major research vehicle for private, foundational, non-profit, academic, and even federally sponsored surveys. Panels are a unique type of survey research platform: Unlike cross-sectional surveys, panels of course recruit respondents specifically for future participation in surveys. In return, panelists are financially compensated, typically to join the panel in the first place, and then secondarily for each survey in which they participate.
These differences to cross-sectional surveys have a range of potential implications. How does the method and effort of recruiting impact who joins, and as a consequence what is best practice? What do panels do to retain panelists over time and which strategies are more successful than others? How much of a concern is panel conditioning, that is, the impact of persons repetitively taking surveys over time, and what are the implications for how frequently panelists should take surveys? How do panels, which exclusively request that panelists take surveys on the Internet, deal with people who do not have or are not comfortable using the Internet? What is the impact of panelist attrition and what are best efforts to replenish retired panelists? How successful are panels are executing true longitudinal surveys? And, given the additional layers of complexity, how are panel surveys properly weighted and estimated?
This short course is designed to provide a guide for consumers of probability-based panels to understand what they are working with: What questions to ask and what features to understand about probability panels in evaluating their use for data collections, and how to best use probability-based panel data. Additionally, it will serve as an exploration of best practices for practitioners: Raising issues of total survey error sources, data quality, costs, and operational logistics.