  There is a growing trend among statistical agencies to explore
non-probability data sources for producing more timely and detailed statistics,
while reducing costs and respondent burden. Coverage and measurement error are
two issues that may be present in such data. The imperfections may be corrected
using available information relating to the population of interest, such as a
census or a reference probability sample.
  In this paper, we compare a wide range of existing methods for producing
population estimates using a non-probability dataset through a simulation study
based on a realistic business population. The study was conducted to examine
the performance of the methods under different missingness and data quality
assumptions. The results confirm the ability of the methods examined to address
selection bias. When no measurement error is present in the non-probability
dataset, a screening dual-frame approach for the probability sample tends to
yield lower sample size and mean squared error results. The presence of
measurement error and/or nonignorable missingness increases mean squared errors
for estimators that depend heavily on the non-probability data. In this case,
the best approach tends to be to fall back to a model-assisted estimator based
on the probability sample.
