Survey Methodology
A note on propensity score weighting method using paradata in survey sampling

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by Seho Park, Jae Kwang Kim and Kimin KimNote 1

  • Release date: December 17, 2019
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Abstract

Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.

Key Words:      Unit Nonresponse; Smoothed weight; Surrogate variable.

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How to cite

Park, S., Kim, J.K. and Kim, K. (2019). A note on propensity score weighting method using paradata in survey sampling. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 3. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2019003/article/00002-eng.htm.

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ISSN : 1492-0921

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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, 2019

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

Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Basic setup
Section 3. Main result
Section 4. Simulation study
Section 5. Application
Section 6. Conclusion
References

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