Open Conference Systems, ITACOSM 2019 - Survey and Data Science

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Combining Non-probability and Probability Survey Samples Through Mass Imputation
Jae-kwang Kim

Building: Learning Center Morgagni
Room: Aula 209
Date: 2019-06-06 05:00 PM – 06:30 PM
Last modified: 2019-05-06


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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