Hi Gu, 

This depends on the context, usually. The question I would ask myself is whether or not "Don't Know" is valid/interesting response to the question being asked or if it just indicates that the survey didn't collect that piece of information. If there *is* a response, but we just didn't observe it, then that's definitely missing. If it's unclear whether or not there is a response, then you may not wan to treat it as missing. 

For imputation, you can include the DKs in the variable and treat it as a categorial/nominal variable for imputation and so Amelia would create a set of dummy variables for each category (1-7 plus DK). What isn't possible in Amelia is to combine ordinal information with the DK. 

Cheers,
Matt


On Sat, Mar 25, 2017 at 4:59 PM, Gu Li <ligu.sysu@gmail.com> wrote:
Dear list members,

I realize that this is perhaps more of a conceptual issue than a practical one, but I wonder how would you deal with survey responses such as "don't know" or "not applicable." Specifically:

(1) Do you regard "don't know" and "not applicable" as missing? 

(2) If not, do you regard them as valid responses as other options (e.g., a scale of 1 to 7), and use all these values to impute missing data? That is, if someone did not answer this item, the imputed value could be don't know, not applicable, or any value from 1 to 7. If this is the correct approach, how to do it in Amelia or other software?

(3)  Is it possible to only impute the "true" missing data (i.e., not for "don't know" or "not applicable" responses), with valid responses from 1 to 7 in Amelia or other software? (Listwise removing participants who select "don't know" or "not applicable" in one variable before imputing is not a good idea because those participants may contribute to MAR/MCAR missing in other variables.)

(4) Are there other approaches to deal with "don't know" or "not applicable" responses?


Many thanks for your help!
Gu

--
Gu Li, PhD
Visiting International Research Scholar
University of British Columbia

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