Hello,
Suppose I have a dataset containing input variables like this:
(Binary) (Interval) (Binary)
HaveAChild AgeOfChild ChildIsMarried
1 12 1
0 . .
0 . .
1 20 0
1 11 1
0 . .
In my predictive modelling, I would like to make use of models such as regression or neural networks, which require complete cases.
However, the AgeOfChild and ChildIsMarried variables are missing for observations where HaveAChild=0, which is expected since there is no child to begin with.
In this case, how can I handle these missing values without discarding them, considering that imputation wouldn't really make sense (e.g. not having a child but having a child age).
Thank you.
Then you should drop these HaveAChild=0, since these obs don't mean anything .
Then I think you should drop variable AgeOfChild ,since this variable is not valid for all obs .
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