I am using a logit model. My dependent variable is binary. However I have an independent variable which is categorical and contains the responses: 1.very good, 2.good, 3.average, 4.poor and 5.very poor
. So, it is ordinal ("quantitative categorical"). I am not sure how to handle this in the model. I am using gretl
.
[Note from @ttnphns: Although the question says the model is logit (because the dependent is categorical), the crucial issue – ordinal independent variables – is basically alike, be the dependent categorical or quantitative. Therefore the question is equally relevant to, say, linear regression too – as it is to logistic regression or other logit model.]
Best Answer
The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotonic" - can be assumed apriori. We have to do something about it, for example - to "screen or to combine variants" or to "prefer what maximizes something".
If you insist on treating your likert rating IV as ordinal (rather than interval or nominal) I've got a pair of alternatives for you.
There could be other suggestions, too. The three above are what come to my mind just instantly reading your question.
Let me recommend you also to visit these threads: Associating between nominal and scale or ordinal; Associating between ordinal and scale. They could be helpful despite that they are not about specifially regressions.
But these threads are about regressions, particularly logistic: you must look inside: one, two, three, four, five.