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Re: st: Nominal or ordinal?
From
Richard Williams <[email protected]>
To
[email protected], "[email protected]" <[email protected]>
Subject
Re: st: Nominal or ordinal?
Date
Fri, 13 Aug 2010 10:32:08 -0400
At 08:42 AM 8/13/2010, Ronan Conroy wrote:
On 12 Lún 2010, at 21:29, David Bell wrote:
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.
I cannot endorse the behaviour of most of the world, which is usually
characterised more by wishful thinking than by reflection.
The assumption of normally distributed error is broken for short
ordinal scales, and I refuse to believe that Extremely Likely (4) is
twice as much belief as Slightly Likely (2). While a scale made up of
many such items will probably exhibit interval properties, this does
not apply to the items themselves.
I am inclined to agree with Ronan. Like I said
before, I don't think ordinal regression is all
that hard to understand, and as Ronan reiterates
assumptions about normally distributed
homoskedastic error terms are clearly violated with short ordinal scales.
Having said that, I find this example interesting:
use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
tab1 warm
reg warm yr89 male white age ed prst
ologit warm yr89 male white age ed prst, nolog
In this example, the T/Z values are virtually
identical across the two methods, and the
coefficients are in the same ratios to each other
(i.e. in the ologit the coefficients are all
about twice as large as they are in the regression).
Of course, this is just one example, and there
are other ordinal variables where it is clearly
unreasonable to think that the categories are
evenly spaced. But this gives hope that nothing
too terrible happens if your typical scale
ranging from "Strongly Disagree" to "Strongly
Agree" is analyzed by ols rather than ordinal regression.
You would think somebody would have done a paper
comparing ols to ordinal regression of such items
- if so, does anybody have a citation?
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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