Examining the Missing Completely at Random Mechanism in Incomplete Data Sets: A Multiple Testing Approach

Structural Equation Modeling

Volumn 19, Issue 3, 2012, Pages 399-408

  Raykov, Tenko ^a   Lichtenberg, Peter A ^b   Paulson, Daniel ^b  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b WAYNE STATE UNIVERSITY   (United States)

Author keywords

false discovery rate; incomplete data; missing completely at random; multiple testing

Indexed keywords

EID: 84864669461     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2012.687660     Document Type: Article

References (22)

1. Agresti, A. and Finlay, B. 2009. Statistical methods for the social sciences, 4th ed. Upper Saddle River, NJ: Prentice-Hall.
2. Allison, P. D. 2001. Missing data. Thousand Oaks, CA: Sage.
3. Arbuckle, J. L. 1996. "Full information estimation in the presence of incomplete data.". In Advanced structural equation modeling: Issues and Techniques, Edited by: Marcoulides, G. A. and Schumacker, R. E. 243-277. Mahwah, NJ: Erlbaum.
4. Baltes, P. B., Dittmann-Kohli, F. and Kliegl, R. 1986. Reserve capacity of the elderly in aging-sensitive tasks of fluid intelligence: Replication and extension. Psychology and Aging, 1: 172-177.
5. Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57: 289-300.
6. Benjamini, Y. and Yekutieli, D. 2001. The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, 29: 1165-1188.
7. Casella, G. and Berger, R. L. 2002. Statistical inference. Monterey, CA: Wadsworth.
8. Collins, L. M., Schafer, J. L. and Kam, C.-M. 2001. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6: 330-351.
9. Conover, W. J. 1999. Practical nonparametric statistics, 3rd ed. New York, NY: Wiley.
10. Enders, C. K. 2010. Applied missing data analysis. New York, NY: Guilford.
11. Graham, J. W. 2003. Adding missing-data relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10: 80-100.
12. Holland, B. 2005. Comment on "False discovery rate-adjusted multiple confidence intervals for selected parameters.". Journal of the American Statistical Association, 100: 89-90.
13. Jamshidian, M. and Jalal, S. 2010. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika, 75: 649-674.
14. Johnson, R. A. and Wichern, D. W. 2002. Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice-Hall.
15. Kim, K. H. and Bentler, P. M. 2002. Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika, 67: 609-624.
16. Little, R. J. A. 1988. A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83: 1198-1202.
17. Little, R. J. A. and Rubin, D. B. 2002. Statistical analysis with missing data. New York, NY: Wiley.
18. Muthén, L. K. and Muthén, B. 2010. Mplus user's guide. Los Angeles, CA: Muthén & Muthén.
19. Raykov, T. 2011. On testability of missing data mechanisms in incomplete data sets. Structural Equation Modeling, 18: 419-430.
20. Raykov, T. and Marcoulides, G. A. 2010. Group comparisons in the presence of missing data using latent variable modeling techniques. Structural Equation Modeling, 17: 135-149.
21. Venables, W. N. and Smith D. M., & The R Development Core Team. 2004. An introduction to R. Bristol, UK: Network Theory Limited.
22. Wasserman, L. 2004. All of statistics. New York: Springer.