Combining Non-probability and Probability Survey Samples Through Mass Imputation
Methodology
2020-11-24 v4
Abstract
This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.
Cite
@article{arxiv.1812.10694,
title = {Combining Non-probability and Probability Survey Samples Through Mass Imputation},
author = {Jae Kwang Kim and Seho Park and Yilin Chen and Changbao Wu},
journal= {arXiv preprint arXiv:1812.10694},
year = {2020}
}
Comments
Submitted to Journal of the Royal Statistical Society: Series A