Survey Methodology
Exchangeability assumption in propensity‑score based adjustment methods for population mean estimation using non‑probability samples

by Yan LiNote 1

  • Release date: June 25, 2024
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Abstract

Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01 ‒ August 04, 2020.

Key Words:  Balancing score; Propensity-score matching; Propensity-score weighting; Quota sample; Taylor linearization variance estimator; SARS-CoV-2 seroprevalence study.

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How to cite

Li, Y. (2024). Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples. Survey Methodology, Statistique Canada, n° 12‑001‑X au catalogue, vol. 50, n° 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2024001/article/00008-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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© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2024

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Catalogue No. 12-001-X

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