627 – Estimation with Nonprobability Samples
Combining Probability and Non-Probability Samples Using Small Area Estimation
Nada Ganesh
NORC at the University of Chicago
Vicki Pineau
NORC at the University of Chicago
Adrijo Chakraborty
NORC at the University of Chicago
J. Michael Dennis
NORC at the University of Chicago
Given the high cost associated with probability samples, there is increasing demand for combining larger non-probability samples with probability samples to increase sample size for low incidence studies and/or key analytic subgroups. Given bias and coverage error inherent in non-probability samples, use of traditional weighted survey estimators for data from such surveys may not be statistically valid. In this paper, we discuss the use of small area models and estimation methods to combine a probability sample with a non-probability sample assuming the (smaller) probability sample yields unbiased estimates. We consider two distinct small area models: (a) Fay-Herriot model with the probability sample point estimate as the dependent variable and the non-probability sample point estimate as a covariate in the model, and (b) Bivariate Fay-Herriot model that jointly models the probability sample point estimate and the non-probability sample point estimate, and accounts for the bias associated with the non-probability sample.