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Abstract
Probability sample (PS) surveys are considered the gold standard for population-based inference but face many challenges due to decreasing response rates, relatively small sample sizes, and increasing costs. In contrast, the use of nonprobability sample (NPS) surveys has increased significantly due to their convenience, large sample sizes, and relatively low costs, but they are susceptible to large selection biases and unknown selection mechanisms. Integrating both sample types in a way that exploits their strengths and overcomes their weaknesses is an ongoing area of methodological research. We build on previous work by proposing a method of supplementing PSs with NPSs to improve analytic inference for logistic regression coefficients and potentially reduce survey costs. Specifically, we use a Bayesian framework for inference. Inference relies on a probability survey with a small sample size, and through the prior structure we incorporate supplementary auxiliary information from a less-expensive (but potentially biased) NPS survey fielded in parallel. The performance of several strongly informative priors constructed from the NPS information is evaluated through a simulation study and real-data application. Overall, the proposed priors reduce the mean-squared error (MSE) of regression coefficients or, in the worst case, perform similarly to a weakly informative (baseline) prior that does not utilize any nonprobability information. Potential cost savings (of up to 68 percent) are evident compared to a probability-only sampling design with the same MSE for different informative priors under different sample sizes and cost scenarios. The algorithm, detailed results, and interactive cost analysis are provided through a Shiny web app as guidance for survey practitioners.
Original language | English |
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Pages (from-to) | 458-492 |
Journal | Journal of Survey Statistics and Methodology |
Volume | 12 |
Issue number | 2 |
Early online date | 17 Nov 2023 |
DOIs | |
Publication status | Published - 1 Apr 2024 |
Keywords
- Bayesian Inference
- Data Integration
- Online Access Panel
- Selection Bias
- Web Survey
- Web survey
- Data integration
- Selection bias
- Bayesian inference
- Online access panel
Fingerprint
Dive into the research topics of 'Bayesian integration of probability and non-probability samples for logistic regression'. Together they form a unique fingerprint.Projects
- 1 Finished
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Statistical Modelling
Onah, C. N. (PI), Shlomo, N. (PI), Chandola, T. (PI), Schoch, D. (PI), Cuitún Coronado, J. (PI), Aparicio-Castro, A. (PI), Thestrup, S. (PI), Olsen, W. K. (PI), Cernat, A. (PI), Shryane, N. (PI), Wisniowski, A. (PI), Smith, D. (PI), Aparicio-Castro, A. (PI), Shafie, T. (PI), Hannemann, T. (PI), Morales-Gómez, A. (PI), Mellon, J. (PI), Troncoso Ruiz, P. (PI), Taub, J. (PI), Murphy, J. (PI), Guest, E. (PI), Kim, J. (PI), Watson, J. (PI), Pashazadeh, F. (PI), Hoór, D. (PI), Jones, P. (PI) & Gosling, Z. (Support team)
1/08/19 → 30/09/22
Project: Research
Research output
- 2 Article
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Integrating Probability and Nonprobability Samples for Survey Inference
Wiśniowski, A., Sakshaug, J., Perez Ruiz, D. & Blom, A. G., 27 Jan 2020, In: Journal of Survey Statistics and Methodology. 8, 1, p. 120–147Research output: Contribution to journal › Article › peer-review
Open Access -
Supplementing Small Probability Samples with Nonprobability Samples: A Bayesian Approach
Sakshaug, J., Wiśniowski, A., Perez Ruiz, D. & Blom, A. G., 9 Sept 2019, (E-pub ahead of print) In: Journal of Official Statistics. 35, 3, p. 653-681Research output: Contribution to journal › Article › peer-review
Open Access