{"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

How do we do Multiple Imputation?<\/h4>\n\n\n\n
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