Before I start, I will point out that I am very new to imputing data and so any advice would be greatly appreciated. Apologies if there is an obvious answer that I am overlooking.
I have a data set for which I am going to be performing multiple imputation to analyse (using the MICE package in R). The main values that are missing are height values. I have enough other variables to work with. They were recorded in either metric or imperial (to be converted to metric). However, for some of the imperial values, the lower denomination is missing. i.e. feet present but no inches.
My first idea was to assume that all of these missing values were 0. However, when I do this and plot a histogram of the heights, there is a huge spike at 5'0". Clearly this original assumption was wrong as this would massively skew my results. Obviously having someone be somewhere between 5'0" and 5'11" is quite a large range which contains most of the population (all female).
It appears that I will have to impute only the lower denomination values for these entries (i.e. inches). Does anybody know of any documentation/articles available which indicate the best way to go about this? Would I impute 4', 5' & 6' separately as if they were different data sets? I feel that this would cause more bias. I'm unsure whether the multiple imputation methods I've read about so far would be able to account for this specific kind of imputation and so I'm hoping for a bit of advice on how to proceed.