Survey Methodology
Integration of data from probability surveys and big found data for finite population inference using mass imputation

by Shu Yang, Jae Kwang Kim and Youngdeok HwangNote 1

  • Release date: June 24, 2021
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Abstract

Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.

Key Words:   Calibration weighting; Data fusion; Generalized additive model; Matching; Nearest neighbor imputation; Post stratification.

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How to cite

Yang, S., Kim, J.K. and Hwang, Y. (2021). Integration of data from probability surveys and big found data for finite population inference using mass imputation. Survey Methodology, Statistics Canada, Catalogue No.  12-001-X, Vol. 47, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2021001/article/00004-eng.htm.

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ISSN : 1492-0921

Editorial policy

Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2021

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Basic setup
Section 3. Methodology
Section 4. Other techniques for mass imputation
Section 5. Regression calibration
Section 6. Empirical experiments
Section 7. Real-data application
Section 8. Discussion
Acknowledgements
Appendix
References

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