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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. Dealing with missing data using stata .
Imputation and Maximum Likelihood using SAS and STATA. The report ends with a summary of other software available for missing data and a list of the useful references that guided this report. Across the report, bear in mind that I will be presenting ‘Second-Best’ solutions to the missing data
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