{"id":277,"date":"2020-05-01T10:48:34","date_gmt":"2020-05-01T10:48:34","guid":{"rendered":"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tessa-wilkie\/?p=277"},"modified":"2020-05-03T17:42:58","modified_gmt":"2020-05-03T17:42:58","slug":"dealing-with-imputation-uncertainty","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tessa-wilkie\/2020\/05\/01\/dealing-with-imputation-uncertainty\/","title":{"rendered":"Dealing with Imputation Uncertainty"},"content":{"rendered":"\n
This post tackles a popular method that helps you understand the amount of variability you have introduced to your analysis through replacing missing data with estimated values. This variability is known as Imputation Uncertainty.<\/p>\n\n\n\n
If you haven\u2019t read my first two posts on Missing Data, it might be worth taking a look before you read this. You can find the first post here<\/a>, and the second, here<\/a>.<\/p>\n\n\n\n I had some misgivings about imputation before I learnt about methods to quantify imputation uncertainty. <\/p>\n\n\n\n My misgivings centred around the fact that with imputation we are sort of making the data up (in a statistically rigorous fashion, of course!). But even so, how happy could we be with our analysis after imputing?<\/p>\n\n\n\n It turns out we can use a method that gives us insight into how much variability is down to the fact that we have imputed missing data.<\/p>\n\n\n\n This can help us to understand how confident we can be in our statistical analysis, given that it is based in part on missing data.<\/p>\n\n\n\n One popular method that gives us a measure of imputation uncertainty is Multiple Imputation.<\/p>\n\n\n\n Multiple Imputation isn’t the only method that can help us with Imputation Uncertainty. You can read more about them in some of the references below.<\/p>\n\n\n\n You can find out more about Imputation Uncertainty in Chapter 5 of the below book. Multiple imputation is discussed in Chapters 5 and 10.<\/p>\n\n\n\n Little, R. J. A. and Rubin, D. B. (2020). Statistical analysis with missing data<\/em>. Wiley Series in Probability and Statistics. Wiley, Hoboken, NJ, third edition.<\/p>\n\n\n\n This paper below contains a nice summary of Multiple Imputation and goes on to discuss the issue of variable selection. In other words, it considers what to do if your different imputed data sets imply that different variables are valuable and should be kept in a statistical model, while others should be discarded.<\/p>\n\n\n\n Wood, A. M., White, I. R., and Royston, P. (2008). How should variable selection be performed with multiply imputed data? Statistics in Medicine<\/em>, 27(17):3227-3246.<\/p>\n\n\n\nHow do we do Multiple Imputation?<\/h4>\n\n\n\n
\n\n\n\nFurther reading<\/h4>\n\n\n\n
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