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Dealing with missing data: Key assumptions and methods …

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.

with 1,000 people and 20 variables. Each of the variables has missing data on 5% of the cases, then, you could expect to have complete data for only about 360 individuals, discarding the other 640. It works well when the data are missing completely at random (MCAR), which rarely happens in reality (Nakai & Weiming, 2011). 4.1.2.

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