00000cam 2200000zi 4500 9.966924 CaOODSP 20261019142944 m o d f cr cn||||||||| 261019e20250908onc obs f000 0 eng d CaOODSP eng rda CaOODSP n-cn--- CS11-522/2024-1-32E-PDF Holmberg, Anders, author. Propensity score estimation and optimal sampling design when integrating probability samples with non-probability data / by Anders Holmberg, Lyndon Ang, Robert Clark and Bronwyn Loong. At head of title: Proceedings of Statistics Canada Symposium 2024 : the future of official statistics [Ottawa] : Statistics Canada = Statistique Canada, September 8, 2025. 1 online resource (9 unnumbered pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier [Statistics Canada international symposium series : proceedings], 1709-8211 Cover title. Issued also in French under title: Estimation du score de propension et plan d’échantillonnage optimal lors de l’intégration d’échantillons probabilistes et de données non probabilistes. Includes bibliographical references (pages [8-9]). "Although non-probability data sources are not new to official statistics, a revived interest in the topic has emerged from pressures due to falling survey response rates, increasing data collection costs and a desire to take advantage of new data source opportunities from the ongoing societal digitalisation. Due to the exclusion of certain segments of the target population, inference derived solely from a non-probability data source is likely to result in bias. This work approaches the challenge of addressing the bias by integrating non-probability data with reference probability samples. The focus will be on methods to model the propensity of inclusion in the non-probability dataset with the help of the accompanying reference sample, with the modelled propensities then applied in an inverse probability weighting approach to produce population estimates. The reference sample is sometimes assumed as given. In this presentation however, an objective of finding an optimal strategy will be pursued that is, the combination of a data integration-based estimator and sample design for the reference probability sample. Recent work is discussed in which advantage is taken of the good unit identification possibilities in business surveys to study an estimator based on propensities and derive optimal (unequal) selection probabilities for the reference sample"--Abstract, page [2]. Statistics Research Canada. Probabilities. Sampling (Statistics) Statistics lcgft Statistics Canada, issuing body. Estimation du score de propension et plan d’échantillonnage optimal lors de l’intégration d’échantillons probabilistes et de données non probabilistes / (CaOODSP)9.966933 Statistics Canada international symposium series - proceedings, 1709-8211 (CaOODSP)9.505681 PDF 505 KB https://publications.gc.ca/collections/collection_2026/statcan/11-522-x/CS11-522-2024-1-32-eng.pdf 11-522-X