Multiple imputation for multivariate missing-data problems: A data analyst's perspective

Multivariate Behavioral Research

Volumn 33, Issue 4, 1998, Pages 545-571

  Schafer, Joseph L ^a   Olsen, Maren K ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0032219074     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1207/s15327906mbr3304_5     Document Type: Review

References (19)

1. Arbuckle, J.L. (1995). Amos Users' Guide. Small Waters, Chicago.
2. Bryk A.S. & Raudenbush, S.W. (1992). Hierarchical Linear Models. Sage, Newbury Park.
3. Bryk, A.S., Raudenbush, S.W., & Congdon, R.T. (1996). Hierarchical Linear and Nonlinear Modeling with the HLM/2L and HLM/3L Programs. Scientific Software International, Chicago.
4. Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
5. Ezzati-Rice, T.M., Johnson, W., Khare M., Little, R.J.A., Rubin, D.B., & Schafer, J.L. (1995). A simulation study to evaluate the performance of model-based multiple imputations in NCHS health examination surveys. In Proceedings of the Annual Research Conference, pp. 257-266. Bureau of the Census, Washington, D.C.
6. Gilks, W.R., Richardson, S., & Spiegelhalter, D.J. (Eds.). (1996). Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
7. Graham, J.W., Hofer, S.M., & Piccinin, A.M. (1994). Analysis with missing data in drug prevention research. In Collins, L. & Seitz, L. (Eds.), National Institute on Drug Abuse Research Monograph Series, Vol. 142, pp. 13-62. National Institute on Drug Abuse, Washington, D.C.
8. Graham, J.W. & Schafer, J.L. (in press). On the performance of multiple imputation for multivariate data with small sample size. In Hoyle, R. (Ed.), Statistical Strategies for Small Sample Research. Sage, Thousand Oaks.
9. Hansen, W.B. & Graham, J.W. (1991). Preventing alcohol, marijuana, and cigarette use among adolescents: Peer pressure resistance training versus establishing conservative norms. Preventive Medicine, 20, 414-430.
10. Littell, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996). SAS System for Mixed Models. SAS Institute, Cary, NC.
11. Little, R.J.A. & Rubin, D.B. (1987). Statistical Analysis with Missing Data. J. Wiley & Sons, New York.
12. Neale, M.C. (1991). Mx: Statistical Modeling. Available from M.C. Neale, Box 3, Department of Human Genetics, Medical College of Virginia, Richmond, VA.
13. Palmer R.F., Graham, J.W., Taylor, B., & Tatterson, J.W. (1998). Interpreting latent variable measurement models in health behavior research. Manuscript submitted for publication.
14. Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581-592.
15. Rubin D.B. (1978). Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse. Proceedings of the Survey Research Methods Section, American Statistical Association, 20-34.
16. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. J. Wiley & Sons, New York.
17. Schafer, J.L. (1997a). Analysis of Incomplete Multivariate Data. Chapman & Hall, London.
18. Schafer J.L. (1997b). Imputation of missing covariates under a multivariate linear mixed model. Technical report 97-10, The Methodology Center, The Pennsylvania State University. Available at http://methcenter.psu.edu.
19. Tanner, M.A. & Wong, W.H. (1987). The calculation of posterior distributions by data augmentation (with discussion). Journal of the American Statistical Association, 82, 528-550