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Investigating an Alternative for Estimation from a Nonprobability Sample: Matching plus Calibration Cover

Investigating an Alternative for Estimation from a Nonprobability Sample: Matching plus Calibration

By: Zhan Liu and  Richard Valliant  
Open Access
|Mar 2023

References

  1. Andridge, R.R., B.T. West, R.J.A. Little, P.S. Boonstra, and F. Alvarado-Leiton. 2019. “Indices of non-ignorable selection bias for proportions estimated from non-probability samples.” Journal of the Royal Statistical Society, 68(5): 1465–1483. Available at: https://doi.org/10.1111/rssc.12371 (accessed September 2022).772461133304001
  2. Baker, R., J.M. Brick, N.A. Bates, M.P. Battaglia, M.P. Couper, J.A. Dever, K. Gile, and R. Tourangeau. 2013. Report of the AAPOR task force on non-probability sampling. The American Association for Public Opinion Research, Deerfield, IL. Available at: https://www.aapor.org/aapor_main/media/mainsitefiles/nps_tf_report_final_7_revised_fnl_6_22_13.pdf (accessed September 2022).
  3. Brick, J.M., and D. Williams. 2013. “Explaining rising nonresponse rates in cross-sectional surveys.” The Annals of the American Academy of Political and Social Science 645(1): 36–59. DOI: https://doi.org/10.1177/0002716212456834.
  4. Caliendo, M, and S. Kopeinig. 2008. “Some practical guidance for the implementation of propensity score matching.” Journal of Economic Surveys 2(1): 31–72. DOI: https://doi.org/10.1111/j.1467-6419.2007.00527.x.
  5. Center for Disease Control and Prevention. 2017. Weighting the BRFSS Data. Available at: https://www.cdc.gov/brfss/annual_data/2017/pdf/weighting-2017-508.pdf.
  6. Center for Disease Control and Prevention. 2023. Behavioral Risk Factor Surveillance Survey. Available at: http://www.cdc.gov/BRFSS.
  7. Chen, Y., P. Li, and C. Wu. 2020. “Doubly robust inference with non-probability survey samples.” Journal of the American Statistical Association 115: 2011–2021. DOI: https://doi.org/10.1080/01621459.2019.1677241.
  8. Cochran, W.G. 1953. “Matching in analytical studies.” American Journal of Public Health 43: 684–691. DOI: https://doi.org/10.2105/AJPH.43.6_Pt_1.684.
  9. Dehejia, R. and S. Wahba. 2002. “Propensity-score matching methods for nonexperi-mental causal studies.” The Review of Economic and Statistics 84(1): 151–161. Available at: http://www.mitpressjournals.org/doi/pdf/10.1162/003465302317331982 (accessed September 2022).10.1162/003465302317331982
  10. Deville, J.C., and C. Sa¨rndal. 1992. “Calibration estimators in survey sampling.” Journal of the American Statistical Association 87(418): 376–382. DOI: https://doi.org/10.2307/2290268.
  11. Elliott, M.R. and R. Valliant. 2017. “Inference for nonprobability samples.” Statistical Science 32: 249–264. Available at: https://projecteuclid.org/journals/statistical-science/volume-32/issue-2/Inference-for-Nonprobability-Samples/10.1214/16-STS598.full. (accessed September 2022).10.1214/16-STS598
  12. Gessendorfer, J., Beste, J., J. Drechsler, and J. Sakshaug. 2018. “Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?. Journal of Official Statistics 34(4): 909–933. DOI: https://doi.org/10.2478/jos-2018-0045.
  13. Hansen, M.H., W.G. Madow, and B.J. Tepping. 1983. “An evaluation of model-dependent and probability sampling inferences in sample surveys.” Journal of the American Statistical Association 78: 776–793.10.1080/01621459.1983.10477018
  14. Kennedy, C., A. Mercer, S. Keeter, N. Hatley, K. McGeeney, and A. Gimenez. 2016. Evaluating online nonprobability surveys, vendor choice matters: widespread errors found for estimates based on blacks and hispanics. Technical report, Pew Research. Available at: https://www.pewresearch.org/methods/2016/05/02/evaluating-online-nonprobability-surveys/ (accessed September 2022).
  15. Kim, J.-K., S. Park, Y. Chen, and C. Wu. 2021. “Combining non-probability and probability survey samples through mass imputation.” Journal of the Royal Statistical Society, Series A: Statistics in Society 184: 941–963. DOI: https://doi.org/10.1111/rssa.12696.
  16. Lee, S. 2006. “Propensity Score Adjustment as a Weighting Scheme for Volunteer Panel Web Surveys.” Journal of Official Statistics 22(2): 329–349. Available at: http://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/propensity-score-adjustment-as-a-weighting-scheme-for-volunteer-panel-web-surveys.pdf (accessed September 2022).
  17. Little, R.J.A., B. West, P.S. Boonstra, and J. Hu. 2019. “Measures of the degree of departure from ignorable sample selection.” Journal of Survey Statistics and Methodology 8(5): 932–964. DOI: https://doi.org/10.1093/jssam/smz023.775089033381610
  18. Lumley, T. 2020. “Survey: analysis of complex survey samples.” R package version 4.1-1. Available at: https://cran.r-project.org/web/packages/survey/index.html (accessed September 2022).
  19. MacKinnon, J.G., and H. White. 1985. “Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties.” Journal of Econometrics 29(3): 305–325. DOI: https://doi.org/10.1016/0304-4076(85)90158-7.
  20. Rao, J., W. Yung, and M Hidiroglou. 2002. “Estimating equations for the analysis of survey data using poststratification information.” Sankhyâ Series A 64: 364–378.
  21. Rivers, D. 2007. “Sample matching for web surveys: Theory and application.” In Proceedings of the Section on Survey Research Methods, July, Salt Lake City, Utah, USA. Available at: http://www.websm.org/uploadi/editor/1368187629Rivers_2007_Sampling_for_web_surveys.pdf (accessed September 2022).
  22. Rivers, D., and D. Bailey. 2009. “Inference from matched samples in the 2008 U.S. national elections.” In Proceedings of the American Statistical Association, Section on Survey Research Methods: 627–639. August, Washington D.C., USA. Available at: http://www.asasrms.org/Proceedings/y2009f.html (accessed September 2022).
  23. Rosenbaum, P.R., and D.B. Rubin. 1983. “The central role of the propensity score in observational studies for causal effects.” Biometrika 70(1): 41–55. Available at: https://doi.org/10.1093/biomet/70.1.41.
  24. Rothman, K.J., S. Greenland, and T.L. Lash. 2008. Modern Epidemiology. Lippincott, Williams and Wilkins, 3rd edition.
  25. Rubin, D. 1973. “Matching to remove bias in observational studies.” Biometrics 29(1): 159–183. DOI: https://doi.org/10.2307/2529684.
  26. Särndal, C.E., B. Swensson, and J.H. Wretman. 1992. Model Assisted Survey Sampling. Springer Series in Statistics. Springer-Verlag, New York.10.1007/978-1-4612-4378-6
  27. Schonlau, M., Soest, A.V., A. Kapteyn, and M. Couper. 2009. “Selection bias in web surveys and the use of propensity scores. Sociological Methods a Research 37(3): 291–318. DOI: https://doi.org/10.1177/0049124108327128.
  28. Sekhon, J.S. 2011. “Multivariate and propensity score matching software with automated balance optimization: The Matching package for R.” Journal of Statistical Software 42(7): 1–52. DOI: https://doi.org/10.18637/jss.v042.i07.
  29. Smith, J., and P. Todd. 2005. “Does matching overcome LaLonde's critique of non-experimental estimators?” Journal of Econometrics 125(1): 305–353. DOI: https://doi.org/10.1016/j.jeconom.2004.04.011.
  30. Terhanian, G., and J. Bremer. 2012. “A smarter way to select respondents for surveys?” International Journal of Market Research 54(6): 751–780. DOI: https://doi.org/10.2501/IJMR-54-6-751-780.
  31. Toluna. 2023. Toluna delivers real-time consumer insights at the speed of the on-demand economy. Available at: https://www.greenbook.org/company/Toluna.
  32. U.S. Census Bureau. 2023. American Community Survey. Available at: https://www.census.gov/programs-surveys/acs.
  33. Valliant, R. 2020. “Comparing alternatives for estimation from nonprobability samples.” Journal of Survey Statistics and Methodology 8: 231–263. DOI: https://doi.org/10.1093/jssam/smz003.
  34. Valliant, R. and J.A. Dever. 2011. “Estimating propensity adjustments for volunteer web surveys.” Sociological Methods and Research 40: 105–137. DOI: https://doi.org/10.1177/0049124110392533.
  35. Valliant, R., J.A. Dever, and F. Kreuter. 2018. Practical Tools for Designing and Weighting Survey Samples. Springer, New York, 2nd edition.10.1007/978-3-319-93632-1
  36. Valliant, R., J.A. Dever, and F. Kreuter. 2020. PracTools: Tools for Designing and Weighting Survey Samples. R package version 1.2.8. Available at: https://CRAN.R-project.org/package=PracTools (accessedSeptember 2022).
  37. Vavreck, L., and D. Rivers. 2008. “The 2006 cooperative congressional election study.” Journal of Elections, Public Opinion and Parties 18(4): 35–66. DOI: https://doi.org/10.1080/17457280802305177.
  38. Wang, L. 2020. Improving External Validity of Epidemiologic Analyses by Incorporating Data from Population-Based Surveys. PhD thesis, University of Maryland. Available at: http://hdl.handle.net/1903/26125, doi:10.13016/pogq-glbs (accessed September 2022).
  39. Wang, L., Graubard, B.I., H. Katki, and Y. Li. 2020. “Improving external validity of epidemiologic cohort analyses: a kernel weighting approach.” Journal of the Royal Statistical Society, Statistics in Society, Series A 183(3):1293–1311. Available at: https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12564 (accessed September 2022).10.1111/rssa.12564756658633071484
  40. Wang, L., R. Valliant, and Y. Li. 2021. “Adjusted logistic propensity weighting methods for population inference using nonprobability volunteer-based epidemiologic cohorts.” Statistics in Medicine: 1–14. Available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.9122 (accessed September 2022).
Language: English
Page range: 45 - 78
Submitted on: Sep 1, 2021
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Accepted on: Oct 1, 2022
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Published on: Mar 16, 2023
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Zhan Liu, Richard Valliant, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.