# How to deal with missing categorical data in logistic regression models?

I am participating in a Project with data from a complex survey. We are going to analyze data from a national fertility survey. Some of the questions in the questionnaire were only asked by a subsample. For example, a question about the job was only asked to employed people. This variable and others like that are important in our study and we would like to include it in our logistic model. What approach do you recommend? I have seen in some papers of this area to set up another category “else”, that is, they combine other people (to which the question was not raised) creating an additional category. If this approach is acceptable, should i attributed random values to the income variable in order to reduce bias?

I will be very grateful if you could help me.

This may not completely answer your question, but, when appropriate, you can use multiple imputations. See the function aregImpute in package rms
• Yours is a special case in which creating a special category of unemployed is 0.9 likely to work, if everyone who is employed answers the job question. In general, creating new categories causes problems, and we use predictive mean matching (the default in aregImpute) to make sure imputations are one of the real categories. – Frank Harrell Apr 6 '14 at 12:53