The Analysis of Social Science Data with Missing Values

Sociological Methods & Research

Volumn 18, Issue 2-3, 1989, Pages 292-326

  Little, Roderick J A ^a   Rubin, Donald B ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84965572693     PISSN: 00491241     EISSN: 15528294     Source Type: Journal    
DOI: 10.1177/0049124189018002004     Document Type: Article

References (43)

1. ALLISON, P. D. (1987) “Estimation of linear models with incomplete data,” pp. 71-103 in Sociological Methodology (annual).
2. AMEMIYA, T. (1984) “Tobit models: a survey.” J. of Econometrics 24: 3-61.
3. ANDERSON, T. W. (1957) “Maximum likelihood estimation for the multivariate normal distribution when some observations are missing.” J. of Amer. Stat. Assn. 52: 200-203.
4. AZEN, S., and M. VAN GUILDER (1981) “Conclusions regarding algorithms for handling incomplete data,” pp. 53-56 in Proceedings of the Survey Research Methods Section, Amer. Stat. Assn.
5. BAKER, S. G., and N. M. LAIRD (1988) “Regression analysis for categorical variables with outcome subject to nonignorable nonresponse.” J. of Amer. Stat. Assn. 81: 29-41.
6. BISHOP, Y.M.M., S. E. FIENBERG, and P. W. HOLLAND (1975) Discrete Multivariate Analysis: Theory and Practice. Cambridge: MIT Press.
7. CHEN, T., and S. E. FIENBERG (1974) “Two-dimensional contingency tables with both completely and partially classified data.” Biometrics 30: 629-642.
8. DAVID, M., R.J.A. LITTLE, M. E. SAMUHEL, and R. K. TRIEST (1986) “Alternative methods of CPS income imputation.” J. of Amer. Stat. Assn. 81: 29-41.
9. DEMPSTER, A. P., N. M. LAIRD, and D. B. RUBIN (1977) “Maximum likelihood from incomplete data via the EM algorithm.” J. of Royal Stat. Society B 39: 1-38.
10. DIXON, W. J. [ed.] (1988) BMDP Statistical Software. Los Angeles: University of California Press.
11. FUCHS, C. (1982) “Maximum likelihood estimation and model selection in contingency tables with missing data.” J. of Amer. Stat. Assn. 77: 270-278.
12. GLYNN, R., N. M. LAIRD, and D. B. RUBIN (1986) “Selection modeling versus mixture modeling with nonignorable nonresponse,” pp. 119-146 in H. Wainer (ed.) Drawing Inferences from Self-Selected Samples. New York: Springer-Verlag.
13. GREENLEES, J. S., W. S. REECE, and K. O. ZIESCHANG (1982) “Imputation of missing values when the probability of response depends on the variable being imputed.” J. of Amer. Stat. Assn. 77: 251-261.
14. HARTLEY, H. O., and R. R. HOCKING (1971) “The analysis of incomplete data.” Biometrics 14: 174-194.
15. HECKMAN, J. (1976) “The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models.” Annals of Economic and Social Measurement 5: 475-492.
16. HEITJAN, D. F., and D. B. RUBIN (1986) “Inference from coarse data using multiple imputation,” pp. 138-143 in T. J. Boardman (ed.) Computer Science and Statistics: Proceedings of the 18th Symposium on the Interface. Arlington, VA: Amer. Stat. Assn.
17. HERZOG, T. N., and D. B. RUBIN (1983) “Using multiple imputations to handle nonresponse in sample surveys,” pp. 210-248 in W. G. Madow, I. Olkin, and D. B. Rubin (eds.) Incomplete Data in Sample Surveys. Vol. 2: Theory and Bibliographies. New York: Academic Press.
18. JENNRICH, R. I., and M. D. SCHLUCHTER (1986) “Imputing for missing survey responses,” pp. 22-31 in Proceedings of the Survey Research Methods Section, Amer. Stat. Assn.
19. KALTON, G., and D. KASPRZYK (1982) “Imputing for missing survey responses,” pp. 22-31 in Proceedings of the Survey Research Methods Section, Amer. Stat. Assn.
20. LI, K. H., X. L. MENG, T. E. RAGHUNATHAN, and D. B. RUBIN (1989) “Significance levels from repeated p-values with multiply imputed data.” Department of Statistics, Harvard University. (Research Report)
21. LI, K. H., T. E. RAGHUNATHAN, and D. B. RUBIN (1988) “Large sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution.” Department of Statistics, Harvard University. (Research Report)
22. LILLARD, L., J. P. SMITH, and F. WELCH (1982) “What do we really know about wages: the importance of nonreporting and census imputation.” J. of Pol. Economy 94: 489-506.
23. LITTLE, R.J.A. (1988a) “Missing data adjustments in large surveys.” J. of Business and Econ. Statistics 6: 1-15.
24. LITTLE, R.J.A. (1988b) “Robust estimation of the mean and covariance matrix from data with missing values.” Applied Statistics 37: 23-38.
25. LITTLE, R.J.A. (1988c) “Incomplete data in event history analysis.” Presented at IUSSP Working Group on Event History Analysis, March 1988, Paris.
26. LITTLE, R.J.A., and D. B. RUBIN (1987) Statistical Analysis with Missing Data. New York: John Wiley & Sons.
27. LITTLE, R.J.A., and H. L. SU (1989) “Item nonresponse in panel surveys,” pp. 400-425 in D. Kasprzyk, G. Duncan, and M. P. Singh (eds.) Panel Surveys. New York: John Wiley & Sons.
28. MADOW, W. G., H. NISSELSON, I. OLKIN, and D. B. RUBIN [eds.] (1983) Incomplete Data in Sample Surveys, Vols. 1-3. New York: Academic Press.
29. McKENDRICK, A. G. (1926) “Applications of mathematics to medical problems.” Proceedings of the Edinburgh Mathematics Society 44: 98-130.
30. MENG, X. L. and D. B. RUBIN (1989) “Obtaining asymptotic variance-covariance matrices in missing-data problems using EM.” Proceedings of the Statistical Computing Section, Amer. Stat. Assn.
31. MUTHEN, B., D. KAPLAN, and M. HOLLIS (1987) “On structural equation modeling with data that are not missing completely at random.” Psychometrika 52: 431-462.
32. OH, H. L., and F. E. SCHEUREN (1980) “Estimating the variance impact of missing CPS income data,” pp. 408-415 in Proceedings of the Survey Research Methods Section, Amer. Stat. Assn.
33. ORCHARD, T., and M. A. WOODBURY (1972) “A missing information principle: theory and applications.” Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability 1: 697-715.
34. RUBIN, D. B. (1974) “Characterizing the estimation of parameters in incomplete data problems.” J. of Amer. Stat. Assn. 69: 467-474.
35. RUBIN, D. B. (1976) “Inference and missing data.” Biometrika 63: 581-592.
36. RUBIN, D. B. (1977) “Formalizing subjective notions about the effect of nonrespondents in sample surveys.” J. of Amer. Stat. Assn. 72: 538-543.
37. RUBIN, D. B. (1978) “Multiple imputations in sample surveys-a phenomenological Bayesian approach to nonresponse,” pp. 20-34 in Proceedings of the Survey Research Methods Section, Amer. Stat. Assn.
38. RUBIN, D. B. (1983) “Iteratively reweighted least squares.” Encyclopedia of the Stat. Sciences 4: 272-275.
39. RUBIN, D. B. (1986) “Statistical matching and file concatenation with adjusted weights and multiple imputations.” J. of Business and Econ. Statistics 4: 87-94.
40. RUBIN, D. B. (1987) Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.
41. RUBIN, D. B., J. L. SCHAFER, and N. SCHENKER (1988) “Imputation strategies for estimating the undercount,” pp. 151-159 in Bureau of the Census Fourth Annual Research Conference. Washington, DC: Department of Commerce.
42. RUBIN, D. B., and N. SCHENKER (1986) “Multiple imputation for interval estimation from simple random samples with ignorable nonresponse.” J. of Amer. Stat. Assn. 81: 366-374.
43. SCHOENBERG, R. S. (1988) “MISS: a program for missing data.” GAUSS Programming Language. Aptech Systems Inc. [P.O. Box 6487, Kent WA 98064]