Re: st: Nominal or ordinal?

[Richard Williams <richardwilliams.ndu@gmail.com>]

I am not so trusting, partly because I don't think ordinal regression 
is all that much harder than ols regression. And, if you do things 
like a plot of residuals versus fitted, it is pretty clear that a 4 
category ordinal dependent variable is not behaving the way you would 
like it to for OLS regression, e.g. OLS assumptions about error terms 
being homoskedastic and normally distributed are clearly violated. Example:
use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
tab1 warm
reg  warm yr89 male white age ed prst
rvfplot

That plot certainly doesn't look like a random scatter of points.

Having said that, if the ordinal variable is independent rather than 
dependent, it is pretty easy to test whether it is ok to treat it as 
continuous.  Long and Freese (in the 2006 book available from Stata 
Press that I referenced earlier) show how in Ch. 9 of their book. 
Basically, you run a model where the variable is treated as 
continuous and another model where you break the variable up into 
dummies. If breaking up into dummies does not significantly improve 
fit you can treat the variable as continuous.
At 04:26 PM 8/12/2010, Michael N. Mitchell wrote:
> Dear Dave (and all others)
>
>   I know I am personally rather trusting of treating such scales as 
> interval data... do you or any others have suggestions on 
> references justifying the treatment of scales like this as interval?
> Many thanks,
>
> Michael N. Mitchell
> Data Management Using Stata      - http://www.stata.com/bookstore/dmus.html
> A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
> Stata tidbit of the week         - http://www.MichaelNormanMitchell.com
>
>
>
> On 2010-08-12 1.29 PM, David Bell wrote:
> > --
> > Chelsea,
> >
> > Most of the world is willing to treat scales like this as interval 
> > data.  Sure it isn't "exactly" interval.  Be sure to consider 
> > whether your audience will be familiar with interpretations of 
> > ordinal logit regressions.
> > Dave
> > ====================================
> > David C. Bell
> > Professor of Sociology
> > Indiana University Purdue University Indianapolis (IUPUI)
> > (317) 278-1336
> > ====================================
> >
> >
> >
> >
> > On Aug 12, 2010, at 2:59 PM, Polis, Chelsea B. wrote:
> >
> > > Dear Statalisters,
> > >
> > > I am working with a dependent variable that has the following four 
> > > potential responses: (1) Not Likely, (2) Slightly Likely,
> > > (3) Quite Likely, (4) Extremely Likely.
> > >
> > > A colleague thinks this is an ordinal variable which should be 
> > > analyzed using ordered logit regression.  My sense was that
> > > this is a nominal variable, and should be analyzed using 
> > > multinomial regression - since we cannot know if the levels are
> > > equally spaced in people's minds.
> > >
> > > My apologies for what is probably a very simplistic question, but 
> > > I've searched Statalist and online, and I still am not
> > > entirely certain.  I would greatly appreciate input on this question.
> > >
> > > Thanks,
> > > Chelsea
> > >
> > >
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-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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EMAIL:  Richard.A.Williams.5@ND.Edu
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