A Look at Propensity-based Methods for Combining Probability and Non-probability Sample Data

Oregon Convention Center 

While the probability sample provides the theoretical backbone for extrapolating from a sample to a population, non-sampling errors such as non-response and measurement errors have made it more challenging and costly to rely solely on probability samples. As a complement, many have proposed using administrative or convenience data which does not have a probabilistic connection to the population but may have richer detail on individuals and may be less costly to acquire. We will present some recent approaches in the literature for combining probability and non-probability samples using propensity-based methods, which attempt to combine the benefits of both sources and mitigate the shortcomings. We highlight motivating applications taken from ecology, public health, and genetics.