Survey Methodology
Comments by Julie Gershunskaya and Vladislav BeresovskyNote 1 on “Handling non‑probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data”

  • Release date: June 25, 2024
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Abstract

Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.

Key Words:   Non-probability sample; Participation probabilities; Sample likelihood; Data combining.

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How to cite

Gershunskaya, J., and Beresovsky, V. (2024). Comments on “Handling non-probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data”. 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/00003-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.

© 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

Frequency: Semi-annual

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