Re: Ordinal logistic regression vs. multiple regression with ordinal outcome?

Thanks for your reply.

David Winsemius wrote:

Wouldn't these just be the same as the reasons to use LR in preference
to ordinary multiple regression for a dichotomous DV?

Yes and no--there's a question of degree here, i.e., how many
categories there are.
10 ordered categories is different than just 2.

WHY LOGISTIC REGRESSION IS NEEDED
One might try to use OLS regression with categorical DVs. There are several
reasons why this is a bad idea:
1. The residuals cannot be normally distributed (as the OLS model assumes),
since they can only take on one of several
values for each combination of level of the IVs

ok

2. The OLS model makes nonsensical predictions, since the DV is not
continuous - e.g., it may predict that someone does
something more than 'all the time'.

Interesting. I'm not sure the issue is one of nonsensical
predications, as much as that the DV is upper- and lower-censored,
while the predicted values aren't.

Wouldn't the same thing happen if the DV is continuous, but has a
natural 0--I mean one where there can be no negative values by
definition?

3. For nominal DVs, the coding is completely arbitrary, and for ordinal
DVs it is (at least supposedly) arbitrary up to a
monotonic transformation. Yet recoding the DV will give very different
results.

I don't agree with this part. One should only consider OLS with an
ordered-category DV if one has reason to believe the categories are
more-or-less evenly spaced (or one uses something like a probit
re-scaling to make them so).
--
John Uebersax PhD

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