Coding "Don't know" in Stata I am coding a questionnaire on STATA and there is a question about Father Education with the option "Don't know" at the end. I am not sure how to code it, because I don't want to count it as missing variable, since the students that answer the questionnaire might have been raised by single moms, etc, and the "don't know" is a valid answer. 
I am wondering if I should code it close to "No Education" as follow:
    recode father_educ2 (3=0 "No schooling")(11=1 "Don't Know")(4=2 "Elementary") ///
      (5=3 "Middle_School")(6=4 "High_School Incomplete")(7=5 "High_School Graduate") ///
      (8=6 "Some College")(9=7 "Bachelor Degree")(10=8 "Graduate Degree") ///
      (1 2 12 13=. "Missing data"), gen(father_educ)

or should I include it after Graduate Degree? any thoughts?
Thanks anyone!
 A: Extending on @whuber's comments, I would code this as an extended missing value:
    recode ... (12 = .d "Don't know") (13 = .r "Refused") ///
      (1 = .n "Not applicable") (2 = .s "Skipped") ...

or something like that. These extended labeled missing values would show up properly in tabulate ..., missing, but would be appropriately excluded from say ordinal logistic regression with father's education as a dependent variable. As far as including these categories in the regression as an explanatory variable, I would probably try to come up with some three-four categories, like "Less than high school", "High school or equivalent", "Some college", "College", "Graduate/professional", may be combining some of these so that you have reasonable 10+% of the data in each category. The missing data, which is probably a small percentage, may go into a separate "Other" category, or, realistically speaking, combined with "Less than high school", because that's what this is likely going to be (at least if you are talking about the US, which you seem to do).
A: It depends on what analysis you want to do, but most likely you should code it as missing, since it is indeed missing. There is a true value of the variable for each such observation, and you do not observe that value. You observe that the student themselves do not know it either, but that information is probably irrelevant to whatever analysis you want to do (e.g., a wage regression).
If your goal was to understand how much students knew about their estranged fathers, then I would create a new binary variable, corresponding to each question about their father, which indicated whether the student knew the answer or not. But probably that's not what you have in mind.
A: As @Aelmore said, the best approach depends on exactly what modeling you are doing, and what substantive questions you are precisely trying to answer.  If you provide more detail, I would feel more comfortable offering suggestions.  Is father's education an independent/predictor variable in a regression?
You raised the possibility of replacing "I don't know" with the mean response.  This is quick and dirty, and I think it is justified in some cases.  Whenever I do that, I also create a put a dummy/indicator variable to note that the variable is missing.
You should also think about imputing the value in a more sophisticated way.  There are many possibilities.
