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# Re: st: Nominal or ordinal?

 From Steve Samuels To statalist@hsphsun2.harvard.edu Subject Re: st: Nominal or ordinal? Date Fri, 13 Aug 2010 07:17:47 -0400

```Oops! Big Midnight Typo.  I meant to write "Ordinal logistic
regression does _not_ require an assumption that categories are
equally spaced."   My apologies..

Steve

On Fri, Aug 13, 2010 at 12:00 AM, Richard Williams
<richardwilliams.ndu@gmail.com> wrote:
> At 08:30 PM 8/12/2010, Steve Samuels wrote:
>>
>> Chelsea,  Ordinal logistic regression does require an assumption that
>> categories are "equally spaced".  Perhaps you are confusing ordinal
>> logistic regression with multiple regression on the category scores.
>>
>> Steve
>
> Steve, I am not sure I am following you (or if I do follow you I don't think
> I agree). According to the Stata 11 Reference Manual, p. 1273:
>
> "The [proportional odds] model is based on the principle that the only
> effect of combining adjoining categories in ordered categorical regression
> problems should be a loss of efficiency in estimating the regression
> parameters."
>
> So,take the original example, where the categories are (1) Not Likely, (2)
> Slightly Likely, (3) Quite Likely, (4) Extremely Likely. Suppose these are
> equally spaced. If the assumptions of the proportional odds model are met,
> you could combine categories 3 and 4, and the model would still work, albeit
> with less efficient estimation. Presumably the categories are no longer
> equally spaced then.
>
> Perhaps I don't understand what you mean by "equally spaced" or maybe there
> is a major gap in my understanding, but I don't recall seeing an "equally
> spaced" requirement for ordinal regression; the outcomes just have to be
> ordered. (Of course, just because the outcomes are ordered does not mean the
> assumptions of the ordered logit model will be met, but that is a different
> issue.)
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
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>
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--
Steven Samuels
sjsamuels@gmail.com
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```