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01 Oct 2013
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access type Open Access

A Simulation Study of Diagnostics for Selection Bias

Online veröffentlicht: 13 Sep 2021
Seitenbereich: 751 - 769
Eingereicht: 01 Jul 2019
Akzeptiert: 01 Nov 2020
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
01 Oct 2013
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

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. Search in Google Scholar

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). Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Heckman, J.J. 1979. “Sample selection bias as a specification error.” Econometrica 47: 153–161. DOI: https://doi.org/10.2307/1912352. Search in Google Scholar

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. Search in Google Scholar

Little, R.J., and D.B. Rubin. 2002. Statistical Analysis with Missing Data. John Wiley & Sons, Hoboken, NJ, 2nd edition. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Search in Google Scholar

Rubin, D.B. 1976. “Inference and missing data.” Biometrika 63: 581–592. DOI: https://doi.org/10.2307/2335739. Search in Google Scholar

Rubin, D.B. 2004. Multiple imputation for nonresponse in surveys, volume 81. John Wiley & Sons. Search in Google Scholar

Särndal, C.-E., and S. Lundström. 2010. “Design for estimation: Identifying auxiliary vectors to reduce nonresponse bias.” Survey Methodology 36: 131–144. Search in Google Scholar

Schouten, B., F. Cobben, J. Bethlehem, et al. 2009. “Indicators for the representativeness of survey response.” Survey Methodology 35: 101–113. Search in Google Scholar

Van Buuren, S., and K. Groothuis-Oudshoorn. 2011. “mice: Multivariate imputation by chained equations in R.” Journal of Statistical Software 45: 1–67. Search in Google Scholar

Wickham, H. 2017. tidyverse: Easily install and load the ‘tidyverse’. R package version 1.2.1 Search in Google Scholar

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. Search in Google Scholar

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