A Note on the Effect of Data Clustering on the Multiple-Imputation Variance Estimator: A Theoretical Addendum to the Lewis et al. article in JOS 2014

Andridge, R.R. 2011. “Quantifying the Impact of Fixed Effects Modeling of Clusters in Multiple Imputation for Cluster Randomized Trials.” Biometrical Journal 53: 57-74. Doi: http://dx.doi.org/10.1002/bimj.201000140.10.1002/bimj.201000140312492521259309Search in Google Scholar

Barnard, J. and D.B. Rubin. 1999. “Small-Sample Degrees of Freedom With Multiple Imputation.” Biometrika 86: 949-955.10.1093/biomet/86.4.948Search in Google Scholar

Box, G.E.P. and G.C. Tiao. 1973. Bayesian Inference in Statistical Analysis. NewYork: Wiley.Search in Google Scholar

Cochran, W.G. 1977. Sampling Techniques. New York: Wiley.Search in Google Scholar

Graham, J., A. Olchowsi, and T. Gilreath. 2007. “How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory.” Prevention Science 8: 206-213. Doi: http://dx.doi.org/10.1007/s11121-007-0070-9.10.1007/s11121-007-0070-917549635Search in Google Scholar

Harel, O. 2007. “Inferences on Missing Information Under Multiple Imputation and Two- Stage Multiple Imputation.” Statistical Methodology 4: 75-89. Doi: http://dx.doi.org/10.1016/j.stamet.2006.03.002.10.1016/j.stamet.2006.03.002Search in Google Scholar

Harel, O. and X.H. Zhou. 2007. “Multiple Imputation: Review of Theory, Implementation, and Software.” Statistics in Medicine 26: 3057-3077. Doi: http://dx.doi.org/10.1002/sim.2787.10.1002/sim.278717256804Search in Google Scholar

Gelman, A., J.B. Carlin, H.S. Stern, and D.B. Rubin. 2004. Bayesian Data Analysis, 2nd ed. New York: CRC Press.10.1201/9780429258480Search in Google Scholar

Kim, J.K. and J. Shao. 2014. Statistical Methods for Handling Incomplete Data. Boca Raton: CRC Press.10.1201/b13981Search in Google Scholar

Kish, L. 1965. Survey Sampling. New York: Wiley.Search in Google Scholar

Lewis, T., E. Goldberg, N. Schenker, V. Beresovsky, S. Schappert, S. Decker, N. Sonnenfeld, and I. Shimizu. 2014. “The Relative Impacts of Design Effects and Multiple 21 Imputation on Variance Estimates: A Case Study With the 2008 National Ambulatory Medical Care Survey.” Journal of Official Statistics 30: 147-161. Doi: http://dx.doi.org/10.2478/jos-2014-0008.10.2478/jos-2014-0008Search in Google Scholar

Little, R.J.A. and D.B. Rubin. 2002. Statistical Analysis of Missing Data. New York: Wiley.10.1002/9781119013563Search in Google Scholar

Lohr, S.L. 2014. “Design Effects for a Regression Slope in a Cluster Sample.” Journal of Survey Statistics and Methodology 2: 97-125. Doi: http://dx.doi.org/10.1093/jssam/smu003.10.1093/jssam/smu003Search in Google Scholar

Meng, X.L. 1995. “Multiple Imputation With Uncongenial Sources of Input (with discussion).” Statistical Science 10: 538-573.Search in Google Scholar

Reiter, J.P., T.E. Raghunathan, and S.K. Kinney. 2006. “The Importance of Modeling the Sampling Design in Multiple Imputation for Missing Data.” Survey Methodology 32: 143-149.Search in Google Scholar

Rubin, D.B. 1978. “Multiple Imputations in Sample Surveys - a Phenomenological Bayesian Approach to Nonresponse.” In Proceedings of the Survey Research Methods Section of the American Statistical Association, date and place, 20-34.Search in Google Scholar

Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: Wiley.10.1002/9780470316696Search in Google Scholar

Rubin, D.B. and N. Schenker. 1986. “Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse.” Journal of the American Statistical Association 81: 366-374. Doi: http://dx.doi.org/10.1080/01621459.1986.10478280.10.1080/01621459.1986.10478280Search in Google Scholar

Schafer, J.L. and R.M. Yucel. 2002. “Computational Strategies for Multivariate Linear Mixed-Effects Models With Missing Values.” Journal of Computational and Graphical Statistics 11: 421-442. Doi: http://dx.doi.org/10.1198/106186002760180608.10.1198/106186002760180608Search in Google Scholar

Schenker, N., T.E. Raghunathan, P.L. Chiu, D.M. Makuc, G. Zhang, and A.J. Cohen. 2006. “Multiple Imputation of Missing Income Data in the National Health Interview Survey.” Journal of the American Statistical Association 101: 924-933. Doi: http://dx.doi.org/10.1198/016214505000001375.10.1198/016214505000001375Search in Google Scholar

Skinner, C.J., D. Hold, and T.F.M. Smith. 1989. Analysis of Complex Surveys. West Sussex: Wiley.Search in Google Scholar

Valliant, R., J.A. Dever, and F. Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York: Springer.10.1007/978-1-4614-6449-5Search in Google Scholar

Valliant, R., A.H. Dorfman, and R.M. Royall. 2000. Finite Population Sampling and Inference: A Prediction Approach. New York: Wiley.Search in Google Scholar

Van Buuren, S. 2012. Flexible Imputation for Missing Data. Boca Raton: CRC Press.10.1201/b11826Search in Google Scholar

Wagner, J. 2010. “The Fraction of Missing Information as a Tool for Monitoring the Quality of Survey Data”, Public Opinion Quarterly 74, 233-243. DOI: http://www.dx.doi.org/10.1093/poq/nfq007. Search in Google Scholar