Results 11 to 20 of about 3,197 (211)
Integrating Probability and Nonprobability Samples for Survey Inference [PDF]
Journal of Survey Statistics and Methodology, 2020 Abstract Survey data collection costs have risen to a point where many survey researchers and polling companies are abandoning large, expensive probability-based samples in favor of less expensive nonprobability samples. The empirical literature suggests this strategy may be suboptimal for multiple reasons, among them that probability ...
Wiśniowski, Arkadiusz; id_orcid 0000-0002-7567-3600 +3 more
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Can Nonprobability Samples be Used for Social Science Research? A cautionary tale
Survey Research Methods, 2019 Survey researchers and social scientists are trying to understand the appropriate use of nonprobability samples as substitutes for probability samples in social science research.
Elizabeth S. Zack +2 more
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We need to talk about nonprobability samples [PDF]
Trends in Ecology & Evolution, 2023 In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance.
Boyd, Robin J. +2 more
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Supplementing Small Probability Samples with Nonprobability Samples: A Bayesian Approach [PDF]
Journal of Official Statistics, 2019 Abstract Carefully designed probability-based sample surveys can be prohibitively expensive to conduct. As such, many survey organizations have shifted away from using expensive probability samples in favor of less expensive, but possibly less accurate, nonprobability web samples.
Sakshaug, Joseph +3 more
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Comparing and Improving the Accuracy of Nonprobability Samples: Profiling Australian Surveys [PDF]
, 2023 There has been a great deal of debate in the survey research community about the accuracy of nonprobability sample surveys. This work aims to provide empirical evidence about the accuracy of nonprobability samples and to investigate the performance of a range of post-survey adjustment approaches (calibration or matching methods) to reduce bias, and ...
Kocar, Sebastian, Baffour, Bernard
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Inference for Nonprobability Samples
Statistical Science, 2017 Although selecting a probability sample has been the standard for decades when making inferences from a sample to a finite population, incentives are increasing to use nonprobability samples. In a world of “big data”, large amounts of data are available that are faster and easier to collect than are probability samples. Design-based inference, in which
Elliott, Michael R., Valliant, Richard
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Reconceptualizing Survey Representativeness for Evaluating and Using Nonprobability Samples [PDF]
Survey Practice, 2013 A question in recent times has been the quality of surveys using nonprobability samples. This paper approaches measurement accuracy by reconceptualizing representativeness. This approach can be used for all samples, probability or not. Current survey designs aim for representative samples with the implicit assumptions that representativeness belongs to
David P. Fan
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Estimation methods for data from nonprobability samples [PDF]
, 2020 The main goal of the present dissertation is to evaluate the asymptotic behaviour of estimators for data from nonprobability samples. In this context some target population units do not have positive inclusion probabilities, which means that estimation is affected by biases associated with under-coverage or self-selection errors.
ROSATI, SIMONA
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Correcting selection bias in nonprobability samples by pseudo weighting
Statistics are often estimated from a sample rather than from the entire population. If the inclusion probability of the sample is unknown to the researcher, that is, a nonprobability sample, naively treating the sample as a simple random sample may result in selection bias.
Liu, A.-C.
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Mathematics, 2021 In the last years, web surveys have established themselves as one of the main methods in empirical research. However, the effect of coverage and selection bias in such surveys has undercut their utility for statistical inference in finite populations. To
Luis Castro-Martín +3 more
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