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Re: st: which statistical analysis to use


From   David Hoaglin <dchoaglin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: which statistical analysis to use
Date   Thu, 19 Apr 2012 07:18:05 -0400

Two quick points.

Whether an off-the-shelf approach is available depends on the shelf.
The literature on analysis of ranking data may have one.  I'm sorry
that I don't know, but I have not needed to analyze such data.

The score is only ordinal outcome, not a real quantification.  Beyond
that, the ranking imposes a constraint.  People are often willing to
treat scores as measurements, but one can't finesse the ranking.

David Hoaglin

On Thu, Apr 19, 2012 at 7:03 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> A more general point is that you are not wedded to the scores as given
> as long as there is a logic to how you treat or re-present them. For
> example, if any skills are graded by 0 by everybody then I am not sure
> you can do much with those except list them. As far as the other
> skills are concerned, you could look at median and quartiles for
> scores as well as mean scores.
>
> Some years ago in an internal discussion about workload weights for
> different kinds of administrative responsibilities we first rejected
> the idea of keeping diaries and quantifying time spent because that
> would be a pain and reward the inefficient and penalise the efficient.
> Then someone who had been reading about Fibonacci numbers said
> something like this. Consider the first few Fibonacci numbers 1, 2, 3,
> 5, 8, 13, 21. Let's have a system in which being Chair of Dept gets
> 21, being in charge of a major area gets 13, and so on down to being
> just a committee member gets 1. This was just plucked out of the air
> as a piece of pure mathematics, but what was interesting was the quick
> consensus was that would produce as good a quantification as any other
> scheme,
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