[
Beaumont, J.F. 2020. “Are probability surveys bound to disappear for the production of official statistics?” Survey Methodology 46: 1–29. Available at: http://www.statcan.gc.ca/pub/12-001-x/2020001/article/00001-eng.htm.
]Search in Google Scholar
[
Beaumont, J.F., and J.N.K. Rao. 2021. “Pitfalls of making inferences from non-probability samples: Can data integration through probability samples provide remedies?” The Survey Statistician 83: 11–22. DOI: https://api.semanticscholar.org/CorpusID:231751185.
]Search in Google Scholar
[
Buelens, B., J. Burger, J.A. van den Brakel. 2018. “Comparing Inference Methods for Non-probability Samples.” International Statistical Review 86: 322–343. DOI: https://doi.org/10.1111/insr.12253.
]Search in Google Scholar
[
Castro-Martín, L.; M.D.M. Rueda, R. Ferri-García. 2021. “Combining statistical matching and propensity score adjustment for inference from non-probability surveys.” Journal of Computational and Applied Mathematics 404: 113414. DOI: https://doi.org/10.1016/j.cam.2021.113414.
]Search in Google Scholar
[
Chen, Y., P. Li., C. Wu. 2019. “Doubly Robust Inference With Nonprobability Survey Samples.” Journal of the American Statistical Association 115:(532): 2011–2021. DOI: https://doi.org/10.1080/01621459.2019.1677241.
]Search in Google Scholar
[
Cornesse, C, A. Blom, D. Dutwin, J. Krosnick, E. de Leeuw, S. Legleye, J. Pasek, D. Pennay, B. Phillips, J. Sakshaug, B. Struminskaya, and A. Wenz. 2020. “A Review of Conceptual Approaches and Empirical Evidence on Probability and Nonprobability Sample Survey Research.” Journal of Survey Statistics and Methodology 8(1): 4–36. DOI: https://doi.org/10.1093/jssam/smz041.
]Search in Google Scholar
[
Daly, M., B. Ebbinghaus, L. Lehner, M. Naczyk, and T. Vlandas. 2020. Oxford Supertracker: The Global Directory for COVID Policy Trackers and Surveys. Department of Social Policy and Intervention. Available at: https://supertracker.spi.ox.-ac.uk/ (accessed September 2023).
]Search in Google Scholar
[
Elliott, M.R., and R. Valliant. 2017. “Inference for nonprobability samples.” Statistical Science 32(2): 249–264. DOI: https://doi.org/10.1214/16-STS598.
]Search in Google Scholar
[
Ferri-García, R., and M.M. Rueda. 2018. “Efficiency of propensity score adjustment and calibration on the estimation from non-probabilistic online surveys.” SORT 42(2): 159–182. DOI: https://doi.org/10.2436/20.8080.02.73.
]Search in Google Scholar
[
Ferri-García, R., and M.M. Rueda. 2022. “Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys.” Statistical Papers 63: 1829–1881. DOI: https://doi.org/10.1007/s00362-022-01296-x.
]Search in Google Scholar
[
Kalton, G. 2023. “Probability vs. Nonprobability Sampling: From the Birth of Survey Sampling to the Present Day.” Statistics in Transition 24(3): 1 – 22. DOI: https://doi.org/10.59170/stattrans-2023-032.
]Search in Google Scholar
[
Kern, C. Y. Li, and L. Wang. 2021. “Boosted Kernel Weighting – Using Statistical Learning to Improve Inference from Nonprobability Samples.” Journal of Survey Statistics and Methodology 9(5): 1088–111. DOI: https://doi.org/10.1093/jssam/smaa028.
]Search in Google Scholar
[
Kim, J.K., and Z. Wang. 2019. “Sampling techniques for big data analysis.” International Statistical Review 87: S177–S191. DOI: https://doi.org/10.1111/insr.12290.
]Search in Google Scholar
[
Lee, S., and R. Valliant. 2009. “Estimation for Volunteer Panel Web Surveys Using Propensity Score Adjustment and Calibration Adjustment.” Sociological Methods and Research 37: 319–343. DOI: https://doi.org/10.1177/0049124108329643.
]Search in Google Scholar
[
Liu, Z., and R. Valliant. 2023. “Investigating an Alternative for Estimation from a Nonprobability Sample: Matching plus Calibration.” Journal of Official Statistics 39:1: 45–78. DOI: http://dx.doi.org/10.2478/JOS-2023-0003.
]Search in Google Scholar
[
Matias, J. N., and A. Leavitt. 2023. COVID-19 social science research tracker. GitHub Repository. Available at: https://github.com/natematias/covid-19-social-science-research (accessed June 2023).
]Search in Google Scholar
[
Rao, J.N.K. 2022. “On Making Valid Inferences by Integrating Data from Surveys and Other Sources.” Sankhya B 83: 242–272. DOI: https://doi.org/10.1007/s13571-020-00227-w.
]Search in Google Scholar
[
Rivers D. 2007. “Sampling for Web Surveys.” In Proceedings of the Joint Statistical Meetings, Salt Lake City, UT, USA, 29 July–2 August. DOI: https://doi.org/10.1002/9781119371717.ch4.
]Search in Google Scholar
[
Sánchez-Cantalejo, C., D. Yucumá, M.M. Rueda, A. Orly, E. Martín, C. Higeras, and A. Cabrera-León. 2023. “Scoping Review of the methodology of large health surveys conducted in Spain early on the COVID-19 pandemic.” Frontiers in Public Health11. DOI: https://doi.org/10.3389/fpubh.2023.1217519.
]Search in Google Scholar
[
Valliant, R. 2020. “Comparing alternatives for estimation from nonprobability samples.” Journal of Survey Statistics and Methodology 8(2): 231–263. DOI: https://doi.org/10.1093/jssam/smz003.
]Search in Google Scholar
[
Wu, C. 2022. “Statistical inference with non-probability survey samples.” Survey Methodology Statistics Canada 48(2). Available at: http://www.statcan.gc.ca/pub/12-001-x/2022002/article/00002-eng.htm.
]Search in Google Scholar
[
Yang, S., and Kim, J.K. 2020. “Statistical data integration in survey sampling: A review.” Japanese Journal of Statistics and Data Science 3: 625–650. Available at: https://link.springer.com/article/10.1007/s42081-020-00093-w.
]Search in Google Scholar
