Missing data imputation versus full information maximum likelihood with second-level dependencies

Structural Equation Modeling

Volumn 18, Issue 4, 2011, Pages 649-662

  Larsen, Ross ^a  

^a UNIVERSITY OF VIRGINIA   (United States)

Author keywords

Full information maximum likelihood; Longitudinal; Missing data; Multilevel analysis; Multiple imputation

Indexed keywords

EID: 84855800994     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2011.607721     Document Type: Article

References (20)

1. Allison, P. (2002). Missing data. Thousand Oaks, CA: Sage.
2. Bodner, T. (2008). What improves with increased missing data imputations? Structural Equation Modeling, 15, 651-675. doi:10.1080/10705510802339072
3. Brown, R. (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. doi:10.1080/10705519409539983
4. Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421-483. doi:10.1177/109442819814004
5. Duncan, S., & Duncan, T. (1994). Modeling incomplete longitudinal substance use data using latent variable growth curve methodology. Multivariate Behavioral Research, 29, 313-338. doi:10.1207/s15327906mbr2904_1
6. Enders, C. (2001). The performance of the full information maximumlikelihood estimator in multiple regressionmodels with missing data. Educational and Psychological Measurement, 61, 713-740. doi:10.1177/0013164401615001
7. Enders, C., & Bandalos, D. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430-457. doi:10.1207/S15328007SEM 0803_5
8. Hartley, H. O., & Rao, J. N. K. (1967). Maximum-likelihood estimation for the mixed analysis of variance model. Biometrika, 54(1/2), 93-108. doi:10.1093/biomet/54.1-2.93
9. Jöreskog, K., & Sörbom, D. (1996). Prelis 2: User's reference guide. Lincolnwood, IL: Scientific Software International.
10. Larsen, R., & Willson, V. (2010). Modeling second level cluster dependencies in multilevel models. Unpublished manuscript.
11. Lavori, P., Dawson, R., & Shera, D. (1995). A multiple imputation strategy for clinical trials with truncation of patient data. Statistics in Medicine, 14, 1913-1925. doi:10.1002/sim.4780141707
12. Meng, X. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9, 538-558.
13. Molenberghs, G., Thijs, H., Jansen, I., Beunckens, C., Kenward, M. G., Mallinckrodt, C., & Carroll, R. J. (2004). Analyzing incomplete longitudinal clinical trial data. Biostat, 5, 445-464. doi:10.1093/biostatistics/5.3.445
14. Newman, D. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6, 328-362. doi:10.1177/1094428103254673
15. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592. doi:10.1093/biomet/63.3.581
16. Rubin, D. B. (1996). Multiple imputation after 18Cyears. Journal of the American Statistical Association, 91, 473-489. doi:10.2307/2291635
17. Rubin, D. B. (2004). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.
18. Ruppert, D. (2004). Statistics and finance: An introduction. New York, NY: Springer.
19. SAS. (1999). SAS manual, release 8 [Computer software manual]. Cary, NC: Author.
20. Schafer, J. (2005, November). Missing data in longitudinal studies: A review. Paper presented at the American Association of Pharmaceutical Scientists, Nashville, TN. Retrieved from http://www.stat.psu.edu/_jls/aaps_schafer.pdf