Multiple imputation of missing covariate values in multilevel models with random slopes: a cautionary note

Behavior Research Methods

Volumn 48, Issue 2, 2016, Pages 640-649

  Grund, Simon ^a   Lüdtke, Oliver ^a   Robitzsch, Alexander ^b  

^a LEIBNIZ INSTITUTE FOR SCIENCE AND MATHEMATICS EDUCATION   (Germany)

^b Federal Institute for Education Research   (Austria)

Author keywords

Covariate; Listwise deletion; Missing data; Multilevel; Multiple imputation; Random slopes

Indexed keywords

ALGORITHM; COMPUTER SIMULATION; HUMAN; SOFTWARE; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; SOFTWARE;

EID: 84928744218     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-015-0590-3     Document Type: Article

References (39)

1. Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39, 1490–1528. doi:10.1177/0149206313478188
2. Allison, P. D. (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research, 28, 301–309. doi:10.1177/0049124100028003003
3. Allison, P. D. (2001). Missing data. Thousand Oaks, CA: Sage.
4. Andridge, R. R. (2011). Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials. Biometrical Journal, 53, 57–74. doi:10.1002/bimj.201000140
5. Bartlett, J. W., Seaman, S. R., White, I. R., & Carpenter, J. R. (2014). Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model. Statistical Methods in Medical Research. doi:10.1177/0962280214521348. Advance online publication.
6. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2013). lme4: Linear mixed-effects models using eigen and s4 (R package version 1.0–5). Retrieved from http://CRAN.R-project.org/package=lme4
7. Carpenter, J. R., & Kenward, M. G. (2013). Multiple imputation and its application (1st ed.). Chichester, UK: Wiley.
8. Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330–351. doi:10.1037/1082-989X.6.4.330
9. Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford Press.
10. Galati, J. C., & Seaton, K. A. (2013). MCAR is not necessary for the complete cases to constitute a simple random subsample of the target sample. Statistical Methods in Medical Research. doi:10.1177/0962280213490360. Advance online publication.
11. Goldstein, H., Carpenter, J. R., & Browne, W. J. (2014). Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms. Journal of the Royal Statistical Society, Series A, 177, 553–564. doi:10.1111/rssa.12022
12. Goldstein, H., Carpenter, J., Kenward, M. G., & Levin, K. A. (2009). Multilevel models with multivariate mixed response types. Statistical Modelling, 9, 173–197. doi:10.1177/1471082X0800900301
13. Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. doi:10.1146/annurev.psych.58.110405.085530
14. Graham, J. W. (2012). Missing data: Analysis and design. New York, NY: Springer.
15. Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206–213. doi:10.1007/s11121-007-0070-9
16. Hochweber, J., Hosenfeld, I., & Klieme, E. (2014). Classroom composition, classroom management, and the relationship between student attributes and grades. Journal of Educational Psychology, 106, 289–300. doi:10.1037/a0033829
17. Hussong, A. M., Cai, L., Curran, P. J., Flora, D. B., Chassin, L. A., & Zucker, R. A. (2008). Disaggregating the distal, proximal, and time-varying effects of parent alcoholism on children’s internalizing symptoms. Journal of Abnormal Child Psychology, 36, 335–346. doi:10.1007/s10802-007-9181-9
18. LaHuis, D. M., & Avis, J. M. (2007). Using multilevel random coefficient modeling to investigate rater effects in performance ratings. Organizational Research Methods, 10, 97–107. doi:10.1177/1094428106289394
19. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: Wiley.
20. Mathieu, J. E., Aguinis, H., Culpepper, S. A., & Chen, G. (2012). Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97, 951–966. doi:10.1037/a0028380
21. Meng, X.-L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9, 538–558.
22. Murray, D. M., & Blitstein, J. L. (2003). Methods to reduce the impact of intraclass correlation in group-randomized trials. Evaluation Review, 27, 79–103. doi:10.1177/0193841X02239019
23. Peters, S. A., Bots, M. L., den Ruijter, H. M., Palmer, M. K., Grobbee, D. E., Crouse, J. R., … Koffijberg, H. (2012). Multiple imputation of missing repeated outcome measurements did not add to linear mixed-effects models. Journal of Clinical Epidemiology, 65, 686–695. doi:10.1016/j.jclinepi.2011.11.012
24. R Development Core Team. (2014). R: A language and environment for statistical computing (Version 3.1.2) [Computer software]. Retrieved from http://www.R-project.org/
25. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
26. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Hoboken, NJ: Wiley.
27. Schafer, J. L. (1997). Imputation of missing covariates under a multivariate linear mixed model (Technical Report #97-04). University Park, PA: Pennsylvania State University, Department of Statistics. Retrieved from http://sites.stat.psu.edu/
28. Schafer, J. L. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistica Neerlandica, 57, 19–35. doi:10.1111/1467-9574.00218
29. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. doi:10.1037/1082-989X.7.2.147
30. Schafer, J. L., & Zhao, J. H. (2014). pan: Multiple imputation for multivariate panel or clustered data (R Package Version 0.9). Retrieved from http://CRAN.R-project.org/package=pan
31. Shin, Y., & Raudenbush, S. W. (2010). A latent cluster-mean approach to the contextual effects model with missing data. Journal of Educational and Behavioral Statistics, 35, 26–53. doi:10.3102/1076998609345252
32. Snijders, T. A. B., & Bosker, R. J. (2012). Components of variance. In T. A. B. Snijders & R. J. Bosker (Eds.), Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed., pp. 114–117). Thousand Oaks, CA: Sage.
33. Taljaard, M., Donner, A., & Klar, N. (2008). Imputation strategies for missing continuous outcomes in cluster randomized trials. Biometrical Journal, 50, 329–345. doi:10.1002/bimj.200710423
34. Twisk, J., de Boer, M., de Vente, W., & Heymans, M. (2013). Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. Journal of Clinical Epidemiology, 66, 1022–1028. doi:10.1016/j.jclinepi.2013.03.017
35. van Buuren, S. (2011). Multiple imputation of multilevel data. In J. J. Hox (Ed.), Handbook of advanced multilevel analysis (pp. 173–196). New York, NY: Routledge.
36. van Buuren, S. (2012). Flexible imputation of missing data. Boca Raton, FL: CRC Press.
37. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67. Retrieved from http://www.jstatsoft.org/.
38. von Hippel, P. T. (2009). How to impute interactions, squares, and other transformed variables. Sociological Methodology, 39, 265–291. doi:10.1111/j.1467-9531.2009.01215.x
39. Yucel, R. M. (2011). Random covariances and mixed-effects models for imputing multivariate multilevel continuous data. Statistical Modelling, 11, 351–370. doi:10.1177/1471082X1001100404