Estimating response propensities in nonprobability surveys using machine learning weighted models

Ramón Ferri-García, Jorge L. Rueda-Sánchez, María del Mar Rueda and Beatriz Cobo

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 225, issue C, 779-793

Abstract: Propensity Score Adjustment (PSA) is a widely accepted method to reduce selection bias in nonprobability samples. In this approach, the (unknown) response probability of each individual is estimated in a nonprobability sample, using a reference probability sample. This, the researcher obtains a representation of the target population, reflecting the differences (for a set of auxiliary variables) between the population and the nonprobability sample, from which response probabilities can be estimated.

Keywords: Propensity score adjustment; Design weights; Nonprobability samples (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:225:y:2024:i:c:p:779-793

DOI: 10.1016/j.matcom.2024.06.012

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