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- Articles and reports: 11-522-X202500100031Description: Several recent quasi-randomization methods for inferences from non-probability samples will be compared. The considered techniques are developed under the assumption that the sample selection is governed by an underlying latent random mechanism and that it can be uncovered by combining non-probability survey data with a "reference" probability-based sample, obtained from the same target population. Challenges prompting the development of alternative procedures include (i) non-probability sample participation indicators are available only on the observed sample units and (ii) it is not generally known which units from the underlying population belong to both the non-probability and reference samples. The ways different procedures address these challenges are considered, theoretical properties of the methods are discussed and their comparison is made using simulations.Release date: 2025-09-08
- Articles and reports: 12-001-X202400100003Description: 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.Release date: 2024-06-25
Articles and reports (2)
Articles and reports (2) ((2 results))
- Articles and reports: 11-522-X202500100031Description: Several recent quasi-randomization methods for inferences from non-probability samples will be compared. The considered techniques are developed under the assumption that the sample selection is governed by an underlying latent random mechanism and that it can be uncovered by combining non-probability survey data with a "reference" probability-based sample, obtained from the same target population. Challenges prompting the development of alternative procedures include (i) non-probability sample participation indicators are available only on the observed sample units and (ii) it is not generally known which units from the underlying population belong to both the non-probability and reference samples. The ways different procedures address these challenges are considered, theoretical properties of the methods are discussed and their comparison is made using simulations.Release date: 2025-09-08
- Articles and reports: 12-001-X202400100003Description: 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.Release date: 2024-06-25