Online Program Home
My Program

Abstract Details

Activity Number: 215 - Evolving Survey Inference in the Big Data Era: Challenges and Opportunities
Type: Invited
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #300279 Presentation
Title: Combining Non-Probability and Probability Survey Samples Through Mass Imputation
Author(s): Jae-kwang Kim* and Seho Park and Yilin Chen and Changbao Wu
Companies: Iowa State University and Dartmouth University and University of Waterloo and University of Waterloo
Keywords: Self-selection; Data Integration; Data fusion; Big data

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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program