Comparing Alternative Estimation Methods Using Combined Probability and Nonprobability Samples

Oregon Convention Center 

Probability sampling may remain as the standard basis for inference from a sample to a population. With declining participation and increasing costs, however, there has been growing interest in combining probability and nonprobability samples to improve the timeliness and cost efficiency of survey estimation without loss of statistical accuracy. An array of estimation methods for combining probability and nonprobability samples are found in the literature. In this paper, we can compare the performance of a group of methods through Monte Carlo simulations. The simulation samples are created from the survey completes of a large-scale national study that employed both probability and nonprobability samples. Five estimation methods are compared, including (1) matching-propensity, (2) division tree model, (3) inverse probability weighting, (4) mass imputation, and (5) doubly robust. The first two methods are developed at NORC, while the other three methods are implemented by the recent R package nonprobsvy. Evaluation metrics include variance, bias, mean square error, and confidence interval coverage.

Key words: Nonprobability sample; Pseudo inclusion probability; Estimation methods;