Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity

Journal of Educational and Behavioral Statistics

Volumn 40, Issue 1, 2015, Pages 69-95

  Drechsler, Jörg ^a  

^a INSTITUTE FOR EMPLOYMENT RESEARCH IAB   (Germany)

Author keywords

fixed effects; Imputation; missing data; multilevel; random effects

Indexed keywords

EID: 84921329943     PISSN: 10769986     EISSN: 19351054     Source Type: Journal    
DOI: 10.3102/1076998614563393     Document Type: Article

References (48)

1. Abowd J. M.,Stinson M.,Benedetto G.Final report to the Social Security Administration on the SIPP/SSA/IRS public use file project. Washington, DC: Bureau of the Census; 2006:
2. Andridge R.Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials.Biometrical Journal. 2011;53:57-74
3. Aßmann C.,Steinhauer H.,Zinn S.Bamberg, Germany: National Educational Panel Study, University of Bamberg; 2012:
4. Zeitschrift für Erziehungswissenschaft (Special Issue 14): Education as a Lifelong Process The German National Educational Panel Study (NEPS). Blossfeld H.-P.Roßbach H.-G.von Maurice J., ed. Wiesbaden, Germany: VS Verlag für Sozialwissenschaften; 2011:
5. Brown E.,Graham J.,Hawkins J.,Arthur M.,Baldwin M.,Oesterle S.Design and analysis of the community youth development study longitudinal cohort sample.Evaluation Review. 2009;33:311-324
6. Bryk A. S.,Raudenbush S. W.Hierarchical linear models: Applications and data analysis methods. London, England: Sage; 1992:
7. Carpenter J.,Goldstein H.,Kenward M.Realcom-impute software for multilevel multiple imputation with mixed response types.Journal of Statistical Software. 2011;45:1-14
8. Clark N.,Shah S.,Dodge J.,Thomas L.,Andridge R.,Awad D.,Little R.An evaluation of asthma interventions for preteen students.Journal of School Health. 2010;80:80-87
9. Clarke P.,Crawford C.,Steele F.,Vignoles A.The choice between fixed and random effects models: Some considerations for educational research. Bristol, England: University of Bristol; 2010:
10. Drechsler J.Multiple imputation in practice—A case study using a complex German establishment survey.Advances in Statistical Analysis. 2011a;95:1-26
11. Drechsler J.New data dissemination approaches in old Europe—Synthetic datasets for a German establishment survey.Journal of Applied Statistics. 2011b;39:243-265
12. Drechsler J.Synthetic datasets for statistical disclosure control—Theory and implementation. New York, NY: Springer; 2011c:
13. Drechsler J.,Bender S.,Rässler S.Comparing fully and partially synthetic data sets for statistical disclosure control in the German IAB Establishment Panel.Transactions on Data Privacy. 2008;1:105-130
14. Drechsler J.,Dundler A.,Bender S.,Rässler S.,Zwick T.A new approach for disclosure control in the IAB Establishment Panel—Multiple imputation for a better data access.Advances in Statistical Analysis. 2008;92:439-458
15. Drechsler J.,Reiter J. P.Sampling with synthesis: A new approach for releasing public use census microdata.Journal of the American Statistical Association. 2010;105:1347-1357
16. Drechsler J.,Reiter J. P.An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets.Computational Statistics & Data Analysis. 2011;55:3232-3243
17. Duchhardt C.,Gerdes A.NEPS technical report for mathematics—Scaling results of starting cohort 4 in ninth grade. National Educational Panel Study, University of Bamberg: Bamberg, Germany; 2013:
18. Gelman A.,Hill J.Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press; 2007:
19. Gelman A.,Rubin D.Inference from iterative simulation using multiple sequences.Statistical Science. 1992;7:457-511
20. Goldstein H.Multilevel statistical models. 2011). Multilevel statistical models ( 4th ed.). West Sussex, England: John Wiley & Sons.West Sussex, England: John Wiley & Sons; 2011:
21. Harville D. A.Matrix algebra from a statistician’s perspective. 1999). Matrix algebra from a statistician’s perspective (2nd ed.). New York, NY: Springer.New York, NY: Springer; 1999:
22. Karr A. F.,Kohnen C. N.,Oganian A.,Reiter J. P.,Sanil A. P.A framework for evaluating the utility of data altered to protect confidentiality.The American Statistician. 2006;60:224-232
23. Kinney S. K.,Reiter J. P.,Reznek A. P.,Miranda J.,Jarmin R. S.,Abowd J. M.Towards unrestricted public use business microdata: The Synthetic Longitudinal Business Database.International Statistical Review. 2011;79:362-384
24. Little R. J. A.,Raghunathan T. E.Proceedings of the Section on Survey Research Methods of the American Statistical Association. Alexandria, VA: American Statistical Association; 1997:617-622.
25. Liu J.,Gelman A.,Hill J.,Su Y.-S.,Kropko J.On the stationary distribution of iterative imputations.Biometrika. 2013;:
26. Liu M.,Taylor J.,Belin T.Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies.Proceedings of the Biometrics Section of the American Statistical Association. 1995;:142-147
27. Meng X.-L.Multiple-imputation inferences with uncongenial sources of input (disc: P558-573).Statistical Science. 1994;9:538-558
28. Pohl S.,Carstensen C. H.NEPS technical report—Scaling the data of the competence tests. National Educational Panel Study, University of Bamberg: Bamberg, Germany; 2012:
29. Raghunathan T. E.,Lepkowski J. M.,van Hoewyk J.,Solenberger P.A multivariate technique for multiply imputing missing values using a series of regression models.Survey Methodology. 2001;27:85-96
30. Reiter J. P.,Raghunathan T. E.,Kinney S. K.The importance of modeling the survey design in multiple imputation for missing data.Survey Methodology. 2006;32:143-150
31. Rubin D. B.Inference and missing data.Biometrika. 1976;63:581-590
32. Rubin D. B.Multiple imputations in sample surveys.Proceedings of the Section on Survey Research Methods of the American Statistical Association. 1978;:20-34
33. Rubin D. B.Multiple Imputation for nonresponse in surveys. New York, NY: John Wiley and Sons; 1987:
34. Schafer J. L.Imputation of missing covariates under a multivariate linear mixed model. State College: The Pennsylvania State University; 1997:
35. Schafer J. L.,Olsen M. K.Multiple imputation for multivariate missing-data problems: A data analyst’s perspective.Multivariate Behavioral Research. 1998;33:545-571
36. Schafer J. L.,Yucel R. M.Computational strategies for multivariate linear mixed-effects models with missing values.Journal of Computational and Graphical Statistics. 2002;11:421-442
37. Schenker N.,Raghunathan T.,Bondarenko I.Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey.Statistics in Medicine. 2010;29:533-545
38. Si Y.,Reiter J. P.Nonparametric bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys.Journal of Educational and Behavioral Statistics. 2013;38:499-521
39. Snijders T.,Bosker R.Multilevel analysis: An introdcution to basic and advanced multilevel modeling. London, England: Sage; 1999:
40. StataCorp. (2011). Accounting for clustering with mi impute. Retrieved February 21, 2014, fromhttp://www.stata.com/support/faqs/statistics/clustering-and-mi-impute/
41. van Buuren S.Handbook of advanced multilevel analysis. Hox J.Roberts J., ed. Milton Park, England: Routledge; 2011:173-196.
42. van Buuren S.,Groothuis-Oudshoorn K.Mice: Multivariate imputation by chained equations in R.Journal of Statistical Software. 2011;45:1-67
43. von Maurice J.,Sixt M.,Blossfeld H.-P.National Educational Panel Study, University of Bamberg: Bamberg, Germany; 2011:
44. Woolridge J.Econometric analysis of cross section and panel data. 2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge, MA: The MIT Press.Cambridge, MA: The MIT Press; 2010:
45. Yucel R.Random covariances and mixed-effects models for imputing multivariate multilevel continuous data.Statistical Modeling. 2011;11:351-370
46. Yucel R. M.,He Y.,Zaslavsky A. M.Using calibration to improve rounding in imputation.The American Statistician. 2008;62:125-129
47. Zhao J. H.,Schafer J. L.Pan: Multiple imputation for multivariate panel or clustered data. 2013:
48. Zhou X.,Reiter J. P.A note on bayesian inference after multiple imputation.The American Statistician. 2010;64:159-163