Good morning everyone,
I am relatively new to regressions using categorical dependent variables and am struggling to decide on the most appropriate specification for my estimation model.
I am analysing the data from four survey questions around job quit intentions (e.g. are you intending to quit your job in the next six months?), three of which have "No" "Yes" "Not Sure" as the potential responses and one of which has "Never" "Occasionally" "Frequently" as the potential responses. n=994. There are 16 IVs in the full specification, some of which are categorical also (e.g. education, salary) and some of which are continuous (e.g. age, average affect scores).
1) Dealing with the three Yes No Not Sure questions first: The Not Sure responses account for 22.6% and 24% of the responses for the first two variables and 5.6% for the third variables. My starting point was therefore that merging the Not Sures with the Nos probably wasn't an option, thus ruling out standard logit / probit. I therefore proceeded to run an ologit model, the results of which successfully met the paralellel regression assumption (Brant test) apart from one of the five subcategories of education which was on the border. Having consulted Long and Freese (2014) in the meantime however, I am now confused as to whether or not ologit is indeed the right model as they clearly state on p.385 that "the category "don't know" invalidates models for ordinal outcomes". My question is whether 'don't know' and 'not sure' responses are equivalent (my instinct says no but happy to be corrected!) and if so, presumably I should be looking to use mlogit instead of ologit?
2) My second question refers to the "Never" "Occasionally" "Frequently" responses. Am I correct in my assumption that these responses constitute ordinal categorical responses and as such should be fitted with an ologit model (given that it passes the Brant test apart from one sub-category of the category education) or should I be considering using gologit2 (or even mlogit as above) instead?
Thanks in advance for your help / advice which is much appreciated
Diane
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