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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: which statistical analysis to use |

Date |
Thu, 19 Apr 2012 12:27:24 +0100 |

I think I agree to all. The ranking literature I have seen by the authors you quoted earlier contains some really cute methods for the case in which all the ranks are distinct (no ties). Then the ranks are permutations of the integers 1 up and group theory and goodness knows what lead to some very smart analyses. Here we are at the opposite end, in which tieing is massive. I wouldn't expect much from that direction. As you say, there are constraints here, so there can only be so many 1s, so many 2s and so forth. That constrains the companies, but it doesn't constrain the skills except indirectly, as I understand it. On Thu, Apr 19, 2012 at 12:18 PM, David Hoaglin <dchoaglin@gmail.com> wrote: > 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, > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: which statistical analysis to use***From:*David Hoaglin <dchoaglin@gmail.com>

**References**:**st: which statistical analysis to use***From:*Deborah Beckers <deborahbeckers@hotmail.com>

**Re: st: which statistical analysis to use***From:*David Hoaglin <dchoaglin@gmail.com>

**RE: st: which statistical analysis to use***From:*Deborah Beckers <deborahbeckers@hotmail.com>

**Re: st: which statistical analysis to use***From:*Nick Cox <njcoxstata@gmail.com>

**RE: st: which statistical analysis to use***From:*Deborah Beckers <deborahbeckers@hotmail.com>

**Re: st: which statistical analysis to use***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: which statistical analysis to use***From:*David Hoaglin <dchoaglin@gmail.com>

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