Applications of Predictive Modeling to Survey Design & Operation in Address-based Samples
AAPOR Webinar Featuring SSRS Chief Methodologist Cameron McPhee
This webinar will discuss the various ways propensity models can be used to improve the design and fielding of surveys using address-based samples (ABS). While the focus of the webinar will be on applications for ABS, much of the information can be applied to other sampling frames including RDD, RBS, and other list frames. We will give a brief, non-technical, overview of the statistical methods used to create propensity models.
However, the majority of the hour will focus on how the models can be applied to different aspects of the survey cycle and will provide examples from recent studies. Specifically, we will discuss using propensity models to oversample lower-responding subgroups or sample units meeting certain eligibility criteria, differentially allocating cash incentives, tailoring mailing materials to appropriate sample units, varying response mode based on predicted preferences, and selectively utilizing more costly nonresponse follow-up strategies based on predicted response and bias propensities. Each example will include a discussion of the tradeoffs associated with the implementation of the propensity modeling.