Missing data often present problems for credible statistical analyses. Luckily there are valid methods for dealing with missing data but the context in which the data are missing can impact the performance of these methods. Relatively little is known about the proper way to handle missing data in multilevel data structures. This study used a Monte Carlo simulation to compare the performance of three missing data methods on multilevel data (multilevel multiple imputation, multiple imputation ignoring the multilevel structure, and listwise deletion). The comparison of these methods was made under conditions known or believed to influence both the performance of missing data methods and multilevel modeling. The results suggest that listwise deletion performs well compared to multilevel multiple imputation but multiple imputation ignoring the multilevel structure performed poorly. The implications of these results for educational research are discussed.
University of Minnesota Ph.D. dissertation. June 2013. Major: Educational Psychology. Advisor: Dr. Michael Harwell. 1 computer file (PDF); xi, 244 pages, appendices A-L.
Medhanie, Amanuel Gebri.
The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models.
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