A New Evaluation of the Impact of Combining Probability and Non-probability Sample Data

Oregon Convention Center 

In recent years, survey researchers have begun to explore the possibility of "sample blending", wherein a questionnaire is administered simultaneously to a probability sample selected randomly from a population and also to a non-probability sample of people who volunteer to complete questionnaires without compensation but have not been selected using any purposing method. A great deal of research shows that probability samples continue to yield highly accurate characterizations of populations, whereas non-probability samples yield notably less accurate measurements. Sample blending involves weighting a non-probability sample to match a probability sample using a handful of variables, with the intent that the weighting will eliminate the inaccuracy of the non-probability sample and yield an effectively larger sample size for much lower cost than would be incurred by collecting exclusively probability sample data. This paper tests the effectiveness of a variety of weighting approaches applied to datasets collected from large probability and non-probability national samples who answered the same long and elaborate questionnaire, which afford opportunities for different analyses.