References
- Albert, A., and J. Anderson. 1984. “On the existence of maximum likelihood estimates in logistic regression models.” Biometrika 71: 1–10. DOI: https://doi.org/10.2307/2336390.10.2307/2336390
- Andridge, R.R., and R.J. Little. 2011. “Proxy pattern-mixture analysis for survey nonresponse.” Journal of Official Statistics 27: 153–180. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/proxy-pattern-mixture-analysis-for-survey-nonresponse.pdf (accessed May 2021).
- Andridge, R.R., and R.J. Little. 2020. “Proxy pattern-mixture analysis for a binary variable subject to nonresponse.” Journal of Official Statistics. DOI: https://doi.org/10.2478/jos-2020-0035.10.2478/jos-2020-0035
- Bootsma-van der Wiel, A.V., E. Van Exel, A. De Craen, J. Gussekloo, A. Lagaay, D. Knook, and R. Westendorp. 2002. “A high response is not essential to prevent selection bias: results from the leiden 85-plus study.” Journal of Clinical Epidemiology 55: 1119–1125. DOI: https://doi.org/10.1016/s0895-4356(02)00505-x.10.1016/S0895-4356(02)00505-X
- 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: 36–59. DOI: https://doi.org/10.1177%2F0002716212456834.10.1177/0002716212456834
- Heckman, J.J. 1979. “Sample selection bias as a specification error.” Econometrica 47: 153–161. DOI: https://doi.org/10.2307/1912352.10.2307/1912352
- Little, R.J. 1994. “A class of pattern-mixture models for normal incomplete data.” Biometrika 81: 471–483. DOI: https://doi.org/10.2307/2337120.10.2307/2337120
- Little, R.J., and D.B. Rubin. 2002. Statistical Analysis with Missing Data. John Wiley & Sons, Hoboken, NJ, 2nd edition.10.1002/9781119013563
- Little, R.J., B.T. West, P. Boonstra, and J. Hu. 2020. “Measures of the degree of departure from ignorable sample selection.” Journal of Survey Statistics and Methodology 8: 932–964. DOI:https://doi.org/10.1093/jssam/smz023.10.1093/jssam/smz023775089033381610
- Mukherjee, B., and N. Chatterjee. 2008. “Exploiting gene-environment independence for analysis of case-control studies: An empirical bayes-type shrinkage estimator to tradeoff between bias and efficiency.” Biometrics 64: 685–694. DOI: https://doi.org/10.1111/j.1541-0420.2007.00953.x.10.1111/j.1541-0420.2007.00953.x18162111
- Nagelkerke, N.J. 1991. “A note on a general definition of the coefficient of determination.” Biometrika 78: 691–692. DOI: https://doi.org/10.1093/biomet/78.3.691.10.1093/biomet/78.3.691
- Nishimura, R., J. Wagner, and M. Elliott. 2016. “Alternative indicators for the risk of non-response bias: a simulation study.” International Statistical Review 84: 43–62. DOI: https://doi.org/10.1111/insr.12100.10.1111/insr.12100487131627212786
- Presser, S., and S. McCulloch. 2011. “The growth of survey research in the United States: Government-sponsored surveys, 1984 – 2004.” Social Science Research 40: 1019–1024. DOI: https://doi.org/10.1016/j.ssresearch.2011.04.004.10.1016/j.ssresearch.2011.04.004
- R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
- Rubin, D.B. 1976. “Inference and missing data.” Biometrika 63: 581–592. DOI: https://doi.org/10.2307/2335739.10.2307/2335739
- Rubin, D.B. 2004. Multiple imputation for nonresponse in surveys, volume 81. John Wiley & Sons.
- Särndal, C.-E., and S. Lundström. 2010. “Design for estimation: Identifying auxiliary vectors to reduce nonresponse bias.” Survey Methodology 36: 131–144.
- Schouten, B., F. Cobben, J. Bethlehem, et al. 2009. “Indicators for the representativeness of survey response.” Survey Methodology 35: 101–113.
- Van Buuren, S., and K. Groothuis-Oudshoorn. 2011. “mice: Multivariate imputation by chained equations in R.” Journal of Statistical Software 45: 1–67.10.18637/jss.v045.i03
- Wickham, H. 2017. tidyverse: Easily install and load the ‘tidyverse’. R package version 1.2.1
- Williams, D., and J.M. Brick. 2018. “Trends in US face-to-face household survey nonresponse and level of effort.” Journal of Survey Statistics and Methodology 6: 186–211. DOI: https://doi.org/10.1093/jssam/smx019.10.1093/jssam/smx019