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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Quintiles |

Date |
Thu, 9 Aug 2012 11:38:35 +0100 |

If I read this correctly, Leonardo agrees that exactly equal frequencies may be impossible with -xtile- but wants to appear to do it exactly by subterfuge, using weights. This can be done: . sysuse auto . xtile qmpg = mpg, n(5) . tab qmpg 5 quantiles | of mpg | Freq. Percent Cum. ------------+----------------------------------- 1 | 18 24.32 24.32 2 | 17 22.97 47.30 3 | 13 17.57 64.86 4 | 12 16.22 81.08 5 | 14 18.92 100.00 ------------+----------------------------------- Total | 74 100.00 . bysort qmpg : gen w = 1/_N . tabstat w , by(qmpg) s(n sum) Summary for variables: w by categories of: qmpg (5 quantiles of mpg) qmpg | N sum ---------+-------------------- 1 | 18 1 2 | 17 1 3 | 13 1 4 | 12 1 5 | 14 1 ---------+-------------------- Total | 74 5 ------------------------------ However, why is exact equality such a big deal here? Why coarsen when you have quantitative information to hand? See also the thread gathered in http://www.stata.com/statalist/archive/2012-06/msg01193.html on how -xtile- on a negated version of a variable may (or may not) work better. Nick On Thu, Aug 9, 2012 at 9:16 AM, Maarten Buis <maartenlbuis@gmail.com> wrote: > On Wed, Aug 8, 2012 at 9:44 PM, Leonardo Jaime Gonzalez Allende wrote: >> I don't was planning to cut a person or household in many parts. The question was about a possible adjustment to the weight factor, if the observation of the sample is the cut point of the quintile. >> >> If I sort the households of a sample by their incomes, a household "x" could represents 300 households but the accumulated frequency of the population is e.g. 20,02%. >> >> My question was if there is an efficient way (command) to repeat the observation and adjust weight factor as follow: >> >> the same household "xa" now represents 280 households and now the accumulated frequency of the population is e.g. 20% (exactly) (leaving to the first quintile). > > What kind of weight did you have in mind, aweigths, pweights, > iweights, fweights? Weighting can be a remarkably tricky issue. There > are many ways such a procedure could go wrong, and I don't know if > there is way to get it right. Anyhow, I cannot imagine a situation > where such an effort would be worth the cost (but that may just as > well say something about a lack of imagination on my part). I would > just live with the fact that the discrete nature of the number of > observations leads to slight variations in group size. > > Did you look at the possibility that ties (different people reporting > exactly the same income) are the source of differences in group size? > In theory, such ties should be pretty rare for a (semi-)continuous > variable like income. However, in practice respondents tend to round > their answers, making such ties a lot more common. * * 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**:**st: One Step and 2 step GMM***From:*muneer <mkmmuneerbabu@gmail.com>

**st: xtreg check for outliers***From:*Dalhia <ggs_da@yahoo.com>

**st: RE: xtreg check for outliers***From:*"Jacobs, David" <jacobs.184@sociology.osu.edu>

**st: Quintiles***From:*Leonardo Jaime Gonzalez Allende <leonardo.gonzalez@ine.cl>

**Re: st: Quintiles***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Quintiles***From:*Maarten Buis <maartenlbuis@gmail.com>

**RE: st: Quintiles***From:*Leonardo Jaime Gonzalez Allende <leonardo.gonzalez@ine.cl>

**Re: st: Quintiles***From:*Maarten Buis <maartenlbuis@gmail.com>

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