The relative performance of full information maximum likelihood estimation for missing data in structural equation models

Structural Equation Modeling

Volumn 8, Issue 3, 2001, Pages 430-457

  Enders, Craig K ^a   Bandalos, Deborah L ^b  

^a UNIVERSITY OF MIAMI   (United States)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000885702     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1207/S15328007SEM0803_5     Document Type: Article

References (27)

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. Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
3. Anderson, T. W. (1957). Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52, 200-203.
4. Arbuckle, J. L. (1995). Amos user's guide [Computer software]. Chicago: Small Waters.
5. 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.
6. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
7. Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika, 50, 229-242.
8. Brown, R. L. (1994). Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods. Structural Equation Modeling, 1, 287-316.
9. 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.
10. Finkbeiner, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44, 409-420.
11. 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.
12. Haitovsky, Y. (1968). Missing data in regression analysis. Journal of the Royal Statistical Society, Series B, 30, 67-82.
13. Hartley, H. O., & Hocking, R. R. (1971). The analysis of incomplete data. Biometrics, 27, 783-823.
14. Jöreskog, K. G., & Sörbom, D. (1983). LISREL VI: Analysis of linear structural relationships by the method of maximum likelihood [Computer software]. Mooresville, IN: Scientific Software International.
15. Jöreskog, K. G., & Sörbom, D. (1993a). LISREL 8 user's reference guide [Computer software]. Chicago: Scientific Software International.
16. Jöreskog, K. G., & Sörbom, D. (1993b). PRELIS 2 user's reference guide [Computer software]. Chicago: Scientific Software International.
17. 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.
18. Kim, J., & Curry, J. (1977). The treatment of missing data in multivariate analyses. Sociological Methods & Research, 6, 215-240.
19. Little, R. J. A. (1992). Regression with missing X's: Areview. Journal of the American Statistical Association, 87, 1227-1237.
20. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
21. Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling, 5, 22-36.
22. Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431-462.
23. Roth, P. L. (1994). Missing data: Aconceptual review for applied psychologists. Personnel Psychology, 47, 537-560.
24. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592.
25. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.
26. Wilks, S. S. (1932). Moments and distributions of estimates of population parameters from fragmentary samples. Annals of Mathematical Statistics, 3, 163-195.
27. 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.