What improves with increased missing data imputations?

Structural Equation Modeling

Volumn 15, Issue 4, 2008, Pages 651-675

  Bodner, Todd E ^a  

^a PORTLAND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONFIDENCE INTERVAL; ESTIMATION PRECISION; INCOMPLETE DATA; MISSING DATA IMPUTATIONS; MISSING INFORMATION; MULTIPLE IMPUTATION; NULL HYPOTHESIS; PRACTICAL GUIDELINES;

STATISTICAL METHODS;

STATISTICAL TESTS;

EID: 54049109688     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705510802339072     Document Type: Article

References (20)

1. Allison, P. (2002). Missing data. Thousand Oaks, CA: Sage.
2. American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.
3. Bodner, T. (2006). Missing data prevalence and reporting practices. Psychological Reports, 99, 675-680.
4. Bodner, T., Deny, A., Haugen, J., McDonald, M., McLeod, A., & Wille, D. (2004, May). A random sample survey of empirical research articles. Poster presented at the annual conference of the American Psychological Society, Chicago.
5. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
6. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm (with, discussion). Journal of the Royal Statistical Society, B, 39, 349-374.
7. Harel, O. (2006, August). Inferences on missing information under multiple imputation. Paper presented at the Joint Statistical Meetings of the American Statistical Association. Seattle, WA.
8. Hershberger, S., & Fisher, D. (2003). A note on determining the number of imputations for missing data. Structural Equation Modeling, 10, 648-650.
9. Loken, E. (2004). Using latent class analysis to model temperament types. Multivariate Behavioral Research, 39, 625-652.
10. Longford, N. (2005). Missing data and small-area estimation: Modern analytical equipment for the survey statistician. New York: Springer.
11. Nickerson, R. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
12. Royston, P. (2004). Multiple imputation of missing values. The Stata Journal, 4, 227-241.
13. Rubin, D. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
14. Ryan, B., & Joiner, B. (1994). Minitab handbook (3rd ed.). Belmont, CA: Wadsworth.
15. Schafer, J. (1997a). Analysis of incomplete multivariate data. New York: Chapman & Hall.
16. Schafer, J. (1997b). NORM Library for S-PLUS [Computer software]. Retrieved August 23, 2001, from http://www.stat.psu.edu/~jls/misoftwa.html
17. Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics (5th ed.). New York: Allyn & Bacon.
18. von Hippel, P. (2005). How many imputations are needed? A comment on Hershberger and Fisher (2003). Structural Equation Modeling, 12, 334-335.
19. von Hippel, P. (2007). Regression with, missing y's: An improved method for analyzing multiply imputed data. Sociological Methodology, 37, 83-117.
20. Wilkinson, L., & The Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.