Coding as a categorical or continuous variable? I have a question/IV in my study which has been answered:
1- No
2- Do not know
3- Sometimes
4- Yes

I was advised to remove the answer 2 (all do not know responses) and to regard these as missing data as there is not much value in this response for the question.
Thus, I ended up recoding with 
1- No
2- Sometimes 
3- Yes

Would this variable be considered continuous or categorical in a binary logistic regression?
Inputting this as it is a continuous factor in a binary logistic regression reveals it is a significant effect: those who scored higher (3) (which would mean yes) were more likely to do the DV behaviour. 
However, I am very doubtful of the statistical correctness of this. 
Should this variable be considered categorical in my regression?
Should I keep the do not know responses? If so, and if I am regarding it as continuous what order of this response (i.e. from 1 to 4) would be most appropriate?
 A: Given these categories as data beyond my control, I would code 
1 No 
2 Sometimes 
3 Yes 
4 Don't know 

on these grounds: 


*

*Sometimes sounds weaker than Yes, which is more emphatic. 

*Don't know doesn't usually belong in an ordered sequence. 
Then some analyses will call for ignoring 4 and some don't. All depends on the question being asked: for example, are you describing the data or modelling? 
But I think it's wrong to call "Don't know" missing. We all answer questionnaires too. If I am allowed to say "Don't know" as one of various possible answers, that is not at all equivalent to my refusing or declining to answer the question. As an occasional survey participant as well as a statistically minded person I object to data being analysed like that. 
There is no case for calling this variable continuous. It's discrete. 1 to 3 alone is ordered, 1 to 4 is just nominal or unordered. 
A context of logistic regression doesn't change how you think about the variable, unless it is being considered as a response and you are choosing between ordinal and multinomial logistic. 
EDIT 
Thinking more about this, it's hard to see that "Sometimes" and "Yes" are mutually exclusive! What are the questions? Do you ever eat meat, drink alcohol, smoke tobacco? 
There is a separate problem if people were presented with these answers in this order: 
1- No
2- Do not know
3- Sometimes
4- Yes

Then it's entirely possible that, rationally or not, some people might regard that as an ordered scale. For example, "Do you approve of the behaviour of prominent politician?". There is something of a case for saying that "Don't know" is in between the extremes, as in "I don't know enough or don't want to be judgmental about the topic". But then people are being expected to know the difference between "Don't know" and "Sometimes". That can happen: I had no idea what was involved in a minor medical condition until it happened to me and was named and explained. 
Without qualitative evidence about how the questionnaire was received or understood, it's very hard to do more than speculate. 
A: (Assuming for simplicity that we're treating "Do not know" as missing:) The three simple approaches are:


*

*Code it as a categorical covariate. If, for instance, you use "No" as the reference level, then you get two coefficients from your regression: a) the log-odds of the outcome (all else being equal, roughly speaking) for a respondent who answers "Sometimes" versus one who answers "No"; and b) the log-odds of the outcome for a respondent who answers "Yes" versus one who answers "No". Disadvantage, you don't get a value for answering "Yes" versus "Sometimes".

*Use it as a continuous variable. Disadvantage, this assumes that the difference between "No" and "Sometimes" is the same magnitude as the difference between "Sometimes" and "Yes".

*I guess you could decide that all you care about is a binary choice -- "Yes" versus "No" or "Sometimes", and re-code it as a boolean variable. But that throws out information.
There are more complex choices as well. This answer has some ideas; and this one, and the links it points to, give you many more.
