Dealing with missing data: Key assumptions and methods …
Technical Report No. 4May 6, 2013Dealing 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.
4.1.1. Listwise deletion (or complete case analysis): If a case has missing data for any of the variables, then simply exclude that case from the analysis. It is usually the default in statistical packages. (Briggs et al.,2003). Advantages: It can be used with any kind of statistical analysis and no special computational methods are required.
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