Combining Non-probability and Probability Survey Samples Through Mass Imputation

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Date
2020-01-09
Authors
Kim, Jae Kwang
Kim, Jae Kwang
Park, Seho
Chen, Yulin
Wu, Changbao
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Altmetrics
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Statistics
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Statistics
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.

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This pre-print is made available through arxiv: https://arxiv.org/abs/1812.10694.

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