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Office of Survey Methods Research
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Published Year: 2024
Survey/Program(s): General Methodology
Catalog: Statistical Survey Paper
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Review of Quasi-Randomization Approaches for Estimation from Non-probability Samples
Abstract
The recent proliferation of computers and the internet have opened new opportunities for collecting and processing data. However, such data are often obtained without a well-planned probability survey design. Such non-probability based samples cannot be automatically regarded as representative of the population of interest. Several classes of methods for estimation and inferences from non-probability samples have been devel-oped in recent years. The quasi-randomization methods assume that non-probability sample selection is governed by an underlying latent random mechanism. The basic idea is to use information collected from a probability (“reference”) sample to uncover latent non-probability survey participation probabilities (also known as “propensity scores”) and use them in estima-tion of target finite population parameters. In this paper, we review and compare theoretical properties of recently developed methods of estimation survey participation probabilities and study their relative performances in simulations.