# Dealing with 'Don't Know' answers for a categorical outcome variable

I have a survey data with categorical outcome variable (yes, no, don't know) which reflects the acceptance of some situation by respondents. My concern is how to deal with Don't know answers, I really doubt I should drop these observations, because:

1. it shrinks my dataset from around 14400 to around 13000, which is considerable;
2. I have intuition that DK answer carry some info and thus not random.

So my questions are:

1. One suggested that non-randomness influences the estimated probability and I should check for it, but how do we check for randomness in Stata?
2. If keeping DK answers is desired then multiple imputation (for example) is the way to deal with this issue. Is there any source/links that I could use to make myself familiar with what multiple imputation is and how it is done in Stata?
3. Almost all papers I read on my topic use logistic regression, I wonder what is the justification behind it. Is there any links/source that compare different probabilistic approaches for not-binary outcome variable (in my case it will be three-asnwers categorical outcome variable) and how we choose between them?
• it shrinks my dataset from around 14400 to around 13000 So, you have about 10% of responses "DK". This isn't really much, so you could drop them to make your life easier. For, if you keep them, they can occur ambiguous: for somebody, DK = in-between, and for somebody, DK = not qualified to answer. – ttnphns Apr 13 '13 at 14:01