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.
Missing data is a problem because nearly all standard statistical methods presume complete information for all the variables included in the analysis. A relatively few absent observations on some variables can dramatically shrink the sample size. As a result, the precision of confidence intervals is harmed, statistical
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