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Dealing with missing data: Key assumptions and methods for ...
Technical Report No. 4 May 6, 2013 Dealing with missing data : Key assumptions andmethods for applied analysisMarina paper was published in fulfillment of the requirements for PM931 Directed Study in Health Policy and Managementunder Professor Cindy Christiansen s direction. Michal Horn y, Jake Morgan, Kyung Min Lee, and Meng-YunLin provided helpful reviews and 1 Contents Executive Summary .. 2 Acronyms .. 3 1. Introduction .. 4 2. missing data mechanisms .. 5 3. Patterns of 6 4. methods for handling missing data .. 6 Conventional methods .. 6 Listwise deletion (or complete case analysis): .. 6 Imputation methods : .. 6 Advanced methods .. 7 Multiple Imputation .. 7 Maximum Likelihood .. 8 Other advanced methods .. 9 Bayesian simulation methods .. 9 Hot deck imputation 10 5. Dealing with missing data using SAS .. 10 Multiple Imputation (MI) .. 11 Maximum Likelihood (ML) .. 13 6.
model using weighted least squares or generalized least squares leads to better results (Graham, 2009) (Allison, 2001) and (Briggs et al., 2003). Limitations of imputation techniques in general: They lead to an underestimation of standard errors and, thus, overestimation of test statistics.
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