Transcription of Dealing with missing data: Key assumptions and methods …
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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 .
report their income, and then testing a difference in mean age. If we fail to reject the null hypothesis, then we can conclude that the MCAR is mostly fulfilled (there could still be some relationship between missingness of Y and the values of Y). b) Missing at random (MAR)-a weaker assumption than MCAR-: The probability of missing data on Y is
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