A Primer on maximum likelihood algorithms available for use with missing data

Structural Equation Modeling

Volumn 8, Issue 1, 2001, Pages 128-141

  Enders, Craig K ^a  

^a UNIVERSITY OF MIAMI   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0002543932     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1207/S15328007SEM0801_7     Document Type: Article

References (24)

1. Allison, P. D. (1987). Estimation of linear models with incomplete data. In C. C. Clogg (Ed.), Sociological methodology, 1987 (pp. 71-103). San Francisco: Jossey-Bass.
2. Arbuckle, J. L. (1995). Amos user's guide [Computer software]. Chicago: Smallwaters.
3. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
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, Ser. B, 39, 1-38.
5. Duncan, T. E., Duncan, S. C., & Li, F. (1998). A comparison of model- and multiple imputation-based approaches to longitudinal analyses with partial missingness. Structural Equation Modeling, 5, 1-21.
6. Efron, B. (1981). Nonparametric estimates of standard error: The jackknife, the bootstrap, and other resampling methods. Biometrika, 68, 589-599.
7. Enders, C. K., & Bandalos, D. L. (in press). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling.
8. Finkbeiner, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44, 409-420.
9. Graham, J. W., & Hofer, S. M. (1993). EMCOV reference manual [Computer software]. Los Angeles: University of Southern California, Institute for Prevention Research.
10. Graham, J. W., Hofer, S. M., & MacKinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31, 197-218.
11. Hartley, H. O., & Hocking, R. R. (1971). The analysis of incomplete data. Biometrics, 27, 783-823.
12. Kaplan, D. (1995). The impact of BIB spiraling-induced missing data patterns on goodness-of-fit tests in factor analysis. Journal of Educational and Behavioral Statistics, 20, 69-82.
13. Kim, J., & Curry, J. (1977). The treatment of missing data in multivariate analyses. Sociological Methods & Research, 6, 215-240.
14. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
15. Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431-462.
16. Neale, M. C. (1995). Mx: Statistical Modeling (3rd ed.) [Computer software]. Richmond, VA: Department of Psychiatry, Medical College of Virginia, Virginia Commonwealth University.
17. Orchard, T., & Woodbury, M. A. (1972). A missing information principle: Theory and applications. Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and Probability, 1, 697-715.
18. Roth, P. L. (1994). Missing data: Aconceptual review for applied psychologists. Personnel Psychology, 47, 537-560.
19. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
20. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
21. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.
22. Schafer, J. L. (1998). NORM: Multiple imputation of incomplete multivariate data under a normal model, version 2 [Computer software]. Software for Windows 95/98/NT. Available: http://www.stat.psu.edu/~jls/misoftwa.html
23. Schafer, J. L., & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst's perspective. Multivariate Behavioral Research, 33, 545-571.
24. Wothke, W. (2000). Longitudinal and multi-group modeling with missing data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples (pp. 219-240). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.