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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Create a normalized variable |

Date |
Thu, 20 Nov 2008 18:58:30 -0000 |

I think everyone can be flexible here according to the problem. I'd be happier with working with the idea that Maarten is around the 99.somethinglarge% percentile of intelligence than with trying to estimate his intelligence on any scale. If I am a pharmacologist (to pick an arbitrary example) I trust the measurement scales my physical and chemical forebears have spent centuries perfecting. I don't want to throw them away as I think that a dose of something means what it says. Nick n.j.cox@durham.ac.uk Maarten buis --- Nick Cox <n.j.cox@durham.ac.uk> wrote: > Roughly speaking, my experience is that natural scientists > (physicists, biologists, etc.) are more likely to take normalised as > meaning scaled to [0, 1], far more commonly by > (value - min) / (max - min) than by percentile ranks. The more > statistics you know, the more likely you are to regard > (value - mean) / sd as a natural standardisation. The funny thing is that (value - mean) / sd scores are often interpreted in terms of percentile ranks: a value of -2 can usually be considered small because, if the variable is reasonable close to a Gaussian distribution, one would expect that approximately 2.5% of the respondent would have a smaller value (and thus 97.5% of the respondents to have a larger value). That looks suspiciously like a percentile rank to me... This is one of the reasons why I think that there is quite some merit in using percentile ranks as a form of standardization. * * 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/

**References**:**RE: st: Create a normalized variable***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**RE: st: Create a normalized variable***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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