Hello,
I am using ice for multiple imputation and have some questions regarding how to handle predicted negative values (for variables that should only be positive, e.g., income) and predicted values that are considerably outside of the range of acceptable values for a given variable.
Several of my variables are cognitive achievement scores for children. When I impute using standardized scores, the means of the imputed variables are extremely different (on the order of 5 times the size) from the mean values for the unimputed versions. When I use the raw scores, the means of the imputed and unimputed versions are very similar, although the range of values continues to be outside the range of allowable values for the variable (e.g., some children are assigned negative cognitive scores). So, I have two questions:
(1) why would the standardized and raw score versions of the variables yield such different imputed values?
(2) what is the appropriate way to deal with imputed values that are outside of the "acceptable" range?
Additionally, is there an option in ice that forces predictions into the range of existing values? And, if so, is it advisable to use it?
Thanks,
Sarah
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