I Have a data set containing about 40 categorical variables. I am trying to factor analyze them. But each categorical variable contains a good number of missing values. Some of them are simply because of non-response. The respondent did not filled up any answer option for that question. Some of them are due to questions of the following type:

5) Do you create formal work teams in your institution?

 1="NO" 2="YES"

(Please skip question number 6 and 7 whose answer to this question is 1="NO")

6) How many members form the work team? (for example)

7) What is the criterion of selecting team members? (for example)

Now those who answered "NO" for question number 5 will not answer 6 and 7. He will again start from 8. This is another source of missing information or gap in the data set. Because of specially this type of missing values if I omit missings listwise a lots of information is missed.

So, I am looking for adjusting these missing values. I don't know how to adjust these missing values (of both non-response and the second type I mentioned) for categorical variables. Taking mean, median or even EM algorithm may not be appropriate for categorical variable I guess. So, what should be done and how?

My actual number of observations is 212, but it reduces to only 42 when I use na.omit(data).


1 Answer 1


As for your non-response case, you might use multiple imputation or, more easy but nevertheless good method, hot-deck imputation. The former is most universal but the latter needs the background variables (the ones by which matching between recipient missing observations and donor non-missing observations takes place) be categorical. Given that your data are mostly categorical, hot-deck method will suit. Both methods are applicable for MAR (missing-at-random) pattern for which listwise deletion or mean/median substitution aren't applicable.

As for your non-questioned case, I believe no special imputation procedure is either helpful or needed. Logically, if a question was not asked because there is single and obvious response (How many members form the work team? - One, me) then you can add this response option as if it were in the questionnaire. But if possible response is ambiguous (What is the criterion of selecting team members? - a) I work single because I'm confident in me; b) I work single because I'm shy to show my incopetence; etc) there's no way out except to drop such questions altogether or to drop everybody not asked such questions.

  • $\begingroup$ Actually primarily I thought I will place 0 for question number 6 because for those respondents who do not have formal work teams, the number of members in the work team (as no team exist for them) can be taken to be 0. But I am not sure if it is statistically correct to do that. Besides, if you tell me how can I perform hot-deck imputation in R or SPSS and give me link to key words about this process I will be grateful. $\endgroup$
    – Blain Waan
    Jul 15, 2012 at 15:55
  • $\begingroup$ Your question 6 looks like a quantitative one. I think 1 member is more reasonable answer then 0, but it's you who decides. SPSS macro for hot-deck imputation - pick it on my web-page. $\endgroup$
    – ttnphns
    Jul 15, 2012 at 16:11
  • $\begingroup$ By the way, Blain, you've already asked several questions on this site. Was you satisfied with any of the answers to mark them as accepted? $\endgroup$
    – ttnphns
    Jul 15, 2012 at 16:21
  • $\begingroup$ Oh! as I am quite new. I didn't notice everything clearly. Thanks for mentioning. $\endgroup$
    – Blain Waan
    Jul 15, 2012 at 16:41

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