Comparison of recent methods for combining probability and non-probability samples

Oregon Convention Center 

Recent proliferation of computers and the internet has opened new opportunities for collecting and processing data. However, such data are often obtained without a well-planned probability survey design. Such non-probability based samples cannot be automatically regarded as representative of the population of interest. Several methods for estimation and inferences from non-probability samples have been developed in recent years. The methods assume that non-probability sample selection is governed by an underlying latent random mechanism. The basic idea is to use information collected from a probability ("reference") sample to uncover latent non-probability survey participation probabilities (also known as "propensity scores") and use them in estimation of target finite population parameters. In this paper, we review several recently developed methods for estimation of non-probability survey participation probabilities. We compare theoretical properties of recently published methods to estimate survey participation probabilities and study their relative performances in simulations.