Survey Methodology
Comments by Michael R. ElliottNote 1 on “Statistical inference with non-probability survey samples”

  • Release date: December 15, 2022
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Abstract

This discussion attempts to add to Wu’s review of inference from non-probability samples, as well as to highlighting aspects that are likely avenues for useful additional work. It concludes with a call for an organized stable of high-quality probability surveys that will be focused on providing adjustment information for non-probability surveys.

Key Words: Pseudo-weighting; Propensity score; Doubly-robust estimation; Sensitivity analysis.

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Elliott, M.R. (2022). Comments on “Statistical inference with non-probability survey samples”.. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 2. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2022002/article/00004-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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Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Additional approaches to combining data from probability and non-probability surveys
Section 3. Weighting vs. modeling for the general user
Section 4. Unverifiable assumptions: Recent developments in sensitivity analysis
Section 5. Probability sampling in the 21st century: Now more than ever
References

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