Efficient handling of predictors and outcomes having missing values

Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis

Volumn , Issue , 2013, Pages 451-479

  Shin, Yongyun ^a  

^a VIRGINIA COMMONWEALTH UNIVERSITY   (United States)

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EID: 85094131643     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (53)

1. Asparouhov, T. and Muthén, B. 2010. Multiple Imputation with Mplus. Technical Report. www.statmodel.com.
2. Browne, W.J. 2012. MCMC estimation in MLwiN Version 2.25. Centre for Multilevel Modelling, University of Bristol.
3. Dempster, A.P., Laird, N.M., and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 76, 1-38.
4. Dempster, A.P., Rubin, D.B., and Tsutakawa, R.K. 1981. Estimation in covariance components models. Journal of the American Statistical Association, 76, 341-353.
5. Diggle, P. and Kenward, M.G. 1994. Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society C, 43, 49-93.
6. Goldstein, H. and Browne, W. 2002. Multilevel factor analysis modelling using Markov Chain Monte Carlo estimation. In G. Marcoulides and I. Moustaki (eds.), Latent Variable and Latent Structure Models, London: Lawrence Erlbaum.
7. Goldstein, H. 2003. Multilevel Statistical Models, London: Edward Arnold.
8. Goldstein, H. and Browne, W. 2005. Multilevel factor analysis models for continuous and discrete data. In A. Olivares and J.J. McArdle (eds.), Contemporary Psychometrics. A Festschrift to Roderick P. McDonald. Mahwah, NJ: Lawrence Erlbaum.
9. Graubard, B.I. and Korn, E.L. 1996. Modelling the sampling design in the analysis of health surveys. Statistical Methods in Medical Research, 5, 263-281.
10. Horton, N.J. and Kleinman, K.P. 2007. Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician, 61, 79-90.
11. Korn, E.L. and Graubard, B.I. 2003. Estimating variance components by using survey data. Journal of the Royal Statistical Society B, 65, 175-190.
12. Krueger, A.B. 1999. Experimental estimates of education production functions. The Quarterly Journal of Economics, 114, 497-532.
13. Li, K., Meng, X., Raghunathan, T.E., and Rubin D.B. 1991. Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1, 65-92.
14. Little, R.J.A. 1995. Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112-1121.
15. Little, R.J.A. and Rubin, D.B. 2002. Statistical Analysis with Missing Data, New York: Wiley.
16. Little, R.J.A. 2009. Selection and pattern-mixture models. In G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs (eds.), Longitudinal Data Analysis (pp. 409-431), Boca Raton, FL: Chapman & Hall/CRC.
17. Lunn, D., Spiegelhalter, D., Thomas, A., and Best, N. 2009. The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28, 3049-3082.
18. Magnus, J.R. and Neudecker, H. 1988. Matrix Differential Calculus with Applications in Statistics and Econometrics, New York: Wiley.
19. Meng, X. and Rubin, D.B. 1992. Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79, 103-111.
20. Meng, X. 1994. Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9, 538-558.
21. Muthén, L. and Muthén, B. 2010. Mplus Users Guide (6th ed.). Los Angeles, CA: Muthén and Muthén.
22. Muthén, B., Asparouhov, T., Hunter, A.M., and Leuchter, A.F. 2011. Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33.
23. OECD. 2007. PISA 2006: Science Competencies for Tomorrow's World, Paris: OECD.
24. Pfeffermann, D., Skinner, C.J., Holmes, D.J., Goldstein, H., and Rasbash, J. 1998. Weighting for unequal selection probabilities in multilevel models. Journal of the Royal Statistical Society B, 60, 23-40.
25. Pinheiro, J.C. and Bates, D.M. 2000. Mixed-Effects Models in S and S-plus, New York: Springer.
26. Raghunathan, T., Lepkowski, J., Van Hoewyk, J., and Solenberger, P. 2001. A mul-tivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27, 85-95.
27. Rasbash, J., Steele, F., Browne, W., and Goldstein, H. 2009. A Users Guide to MLwiN Version 2.10, Centre for Multilevel Modelling, University of Bristol.
28. Raudenbush, S.W. and Bryk, A.S. 2002. Hierarchical Linear Models, Newbury Park, CA: Sage.
29. Raudenbush, S.W., Bryk, A.S., Cheong, Y., Congdon, R.T., and Mathilda du Toit. 2011. HLM 7: Hierarchical Linear and Nonlinear Modeling. Lincolnwood, IL: Scientific Software International.
30. Rubin, D.B. 1976. Inference and missing data. Biometrika, 63, 581-592.
31. Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys, New York: J. Wiley & Sons.
32. Rubin, D.B. 1996. Multiple imputation after 18+ years. JASA, 91, 473-489.
33. Rubin, D.B. 2003. Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57, 3-18.
34. Schafer, J.L. 1997. Analysis of Incomplete Multivariate Data, London: Chapman & Hall.
35. Schafer, J.L. 1999. NORM: Multiple Imputation of Incomplete Multivariate Data under a Normal Model [Computer Software]. University Park: Pennsylvania State University, Department of Statistics.
36. Schafer, J.L. and Yucel, R.M. 2002. Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11, 437-457.
37. Schafer, J.L. 2003. Multiple imputation in multivariate problems when imputation and analysis models differ. Statistica Neerlandica, 57, 19-35.
38. Shin, Y. 2003. Inference and Applications in Hierarchical Linear Models with Missing Data. Unpublished doctoral dissertation, University of Michigan.
39. Shin, Y. 2012. Do black children benefit more from small classes? Multivariate instrumental variable estimators with ignorable missing data. Journal of Educational and Behavioral Statistics, 37, 543-574.
40. Shin, Y. and Raudenbush, S.W. 2007. Just-identified versus over-identified two-level hierarchical linear models with missing data. Biometrics, 63, 1262-1268.
41. Shin, Y. and Raudenbush, S.W. 2010. A latent cluster mean approach to the contextual effects model with missing data. Journal of Educational and Behavioral Statistics, 35, 26-53.
42. Shin, Y. and Raudenbush, S.W. 2011. The causal effect of class size on academic achievement: Multivariate instrumental variable estimators with data missing at random. Journal of Educational and Behavioral Statistics, 36, 154-185.
43. Shin, Y. and Raudenbush, S.W. 2013. Efficient analysis of Q-level nested hierarchical general linear models given ignorable missing data. International Journal of Biostatistics (to appear).
44. Snijders, T.A.B. and Bosker, R.J. 2012. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, London: Sage.
45. Spiegelhalter, D.J., Thomas, A., and Best, N.G. 2000. WinBUGS version 1.3: User manual. Medical Research Council Biostatistics Unit, Cambridge.
46. Stram, D.O. and Lee, J.W. 1994. Variance components testing in the longitudinal mixed effects model. Biometrics, 50, 1171-1177.
47. Tourangeau, K., Nord, C., Le, T., Sorongon, A.G., and Najarian, M. 2009. Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K), Combined User's Manual for the ECLS-K Eighth-Grade and K-8 Full Sample Data Files and Electronic Codebooks (NCES 2009-004). Washington, DC: NCES, IES, DOE.
48. Van Buuren, S., Brand, J., Groothuis-Oudshoorn, C., and Rubin, D. 2006. Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76, 1049-1064.
49. Van Buuren, S. and Groothuis-Oudshoorn, K. 2011. MICE: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1-67.
50. Van Buuren, S. 2011. Multiple imputation of multilevel data. In J.J. Hoxand and J.K. Roberts. (eds.), The Handbook of Advanced Multilevel Analysis, Chapter 10, pp. 173-196. Milton Park, UK: Routledge.
51. Verbeke, G. and Molenberghs, G. 2000. Linear Mixed Models for Longitudinal Data, New York: Springer.
52. Yuan, Y.C. 2000. Multiple imputation for missing data: Concepts and new development. In Proceedings of the Twenty-Fifth Annual SAS Users Group International Conference (Paper No. 267). Cary, NC: SAS Institute.
53. Yucel, R.M. 2008. Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. Philosophical Transactions of the Royal Society A, 366, 2389-2403.