Transcription of Dealing with missing data: Key assumptions and methods for ...
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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 .
(Briggs et al.,2003). Advantages: It can be used with any kind of statistical analysis and no special computational methods are required. Limitations: It can exclude a large fraction of the original sample. For example, suppose a data set with 1,000 people and 20 variables. Each of the variables has missing data on 5% of the cases,
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