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RE: st: RE: Mean test in a Likert Scale
Cameron McIntosh <firstname.lastname@example.org>
STATA LIST <email@example.com>
RE: st: RE: Mean test in a Likert Scale
Sat, 1 Sep 2012 17:03:57 -0400
And what would be wrong with just using some form of ordinal or multinomial regression?
> Date: Sat, 1 Sep 2012 12:48:20 -0700
> Subject: Re: st: RE: Mean test in a Likert Scale
> From: firstname.lastname@example.org
> To: email@example.com
> David, if we return to your original question, I believe that the use
> of original numerical values of ordinal variables is a very serious
> error, but it has nothing to do with transformation of variables.
> To examplify why this is a serious mistake, consider the following
> example taken from Kmenta (1997):
> Suppose our dependent variable is the level of wage and our
> independent variable is the level of education with 3 categories: D= 0
> for elementary-school education, D=1 for high school education and D=2
> for academic education.
> If you run regression between the wage level and D, your implicit
> assumption is that wage difference between D=0 and D=1 is equal to the
> wage difference between D=1 and D=2. This is a wrong and very limited
> Moreover, if for some reason you have decided that the scale of D will
> be D= 0 for elementary-school education, D=10 for high school
> education and D=20 for academic education, then the implicit
> assumption becomes that the wage of D=20 is ten time bigger than the
> wage of D=10. In other words, the ordinal numerical value, which does
> not have any quantitative interpretation, have an impact on the
> regression outcomes. This is the reason we need to use binary
> variables under such circumstances.
> To see the correct analysis of questionnaires with ordinal questions,
> you can take a look at my paper published in urban studies:
> On Sat, Sep 1, 2012 at 9:32 AM, Nick Cox <firstname.lastname@example.org> wrote:
> > My examples -- miles per gallon, price, weight -- all qualify as ratio scales.
> > If I understand you correctly, your view is that transformations are
> > illegitimate in statistics.
> > Is that right?
> > How about link functions as in -glm-? Transformed scales on graphs?
> > Nick
> > On Sat, Sep 1, 2012 at 3:50 PM, Ulrich Kohler <email@example.com> wrote:
> >> Am Samstag, den 01.09.2012, 02:16 +0100 schrieb Nick Cox:
> >>> But this objection is so strong that it rules out taking out means in
> >>> most circumstances, not just for ordinal scales.
> >>> It's clearly true that mean of transform is not transform of mean
> >>> unless that transform is a linear function. The same argument would
> >>> imply that means are invalid for measured variables (e.g. means of
> >>> miles per gallon, weight, price in the auto data) because they are not
> >>> equivariant under transformation. Both theory and practice tell us
> >>> that means, geometric means, harmonic means, etc. can all make some
> >>> sense for many measured variables. Poisson regression and generalised
> >>> linear models all hinge on this.
> >> Sorry but I disaggree here. For an intervall scale a transformation such
> >> as the one that I used in my example are not allowed because it would
> >> obvioulsy distroy the equal distance characteristic of subsequent
> >> values. For an intervall scale only linear transformations are allowed
> >> and therfore substantive conclusions taken from the mean are robust for
> >> arbitrary _allowed_ transformation of the intervall scale.
> >>> There's also a big difference of viewpoint here. Measurement theory
> >>> loves these arguments about arbitrary order-preserving
> >>> transformations, but I don't think they make much sense to scientists
> >>> who actually do measurements.
> >> But I don't think we -- that is me and you -- disaggree here. In way
> >> that's what I wanted to say when I said that an ordinal scale could be
> >> taken as kind of an "conventional" absolute scale in some instances.
> >> *
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> Dr. Yuval Arbel
> School of Business
> Carmel Academic Center
> 4 Shaar Palmer Street,
> Haifa 33031, Israel
> e-mail1: firstname.lastname@example.org
> e-mail2: email@example.com
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