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From |
Barbro Widerstedt <appoloniak@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: means compairison with weights and unequal variance |

Date |
Wed, 23 Nov 2011 18:15:12 +0100 |

Sorry, I did not see your earlier answer -- for some reason your mails are archived as spam in gmail. Thank you, I'll consider your answer. And try to de-spam you :) On Tue, Nov 22, 2011 at 9:40 AM, Nick Cox <njcoxstata@gmail.com> wrote: > See also the suggestions made earlier in the thread at > > http://stata.com/statalist/archive/2011-11/msg01010.html > > Nick > > On Tue, Nov 22, 2011 at 8:16 AM, Barbro Widerstedt <appoloniak@gmail.com> wrote: >> Now I feel a bit stupid -- of course. It is what I do for other >> outcomes, and the strategy should be as valid in this case... I'll >> have a look and see if they give me the same conclusion at p<0.05 >> >> >> >> On Mon, Nov 21, 2011 at 7:45 PM, Ariel Linden, DrPH >> <ariel.linden@gmail.com> wrote: >>> Why not simply use -regress- and the weight generated in -cem- (cem_weights) >>> as the aweight with robust se? This is the approach suggested by the >>> authors. See: >>> >>> Stefano M. Iacus, Gary King, and Giuseppe Porro, "Matching for Causal >>> Inference Without Balance Checking", copy at >>> <http://gking.harvard.edu/files/abs/cem-abs.shtml> >>> >>> Ariel >>> >>> >>> >>> Date: Sun, 20 Nov 2011 21:40:40 -0800 >>> From: John Luke Gallup <jlgallup@pdx.edu> >>> Subject: Re: st: means compairison with weights and unequal variance >>> >>> Barbro, >>> >>> A simple alternative is to calculate the means and standard deviations for >>> each group using -summarize- with weights, and then run -ttesti ..., >>> unequal-: >>> >>> sysuse auto, clear >>> >>> sum mpg if foreign [aw=weight] >>> local N1 = r(N) >>> local av1 = r(mean) >>> local sd1 = r(sd) >>> >>> sum mpg if !foreign [aw=weight] >>> local N2 = r(N) >>> local av2 = r(mean) >>> local sd2 = r(sd) >>> >>> ttesti `N1' `av1' `sd1' `N2' `av2' `sd2', unequal >>> >>> John >>> >>> John Luke Gallup >>> Department of Economics >>> Portland State University >>> >>> On Nov 20, 2011, at 2:13 AM, appoloniak wrote: >>> >>>> Hello statslisters, >>>> >>>> [caveat: sorry if this is a FAQ, but sometimes my imagination in >>>> creating queries for use in the archives gives me nothing... and it is >>>> more of a statistics that a Stata question, so please don't hit me too >>>> hard ... ] >>>> >>>> I have a dataset where I try to compare the means of a variable >>>> between two groups (treated and untreated). >>>> The data set used is a sample, drawn from the superpopulation by the >>>> ado-package cem (Iaucus et al Coarsened enhanced mathing), and >>>> subsequent estimations should be weighted. >>>> >>>> This means that a standard t-test cannot be used, and I searched a bit >>>> and found that <oneway> is an alternative with weighted data. However, >>>> the groups have unequal variance which is a problem for <oneway> (at >>>> least I think so, I know ANOVA mainly by name ...). I read one entry >>>> that suggests that oneway is robust to groupwise unequal variance if >>>> groupsize does not vary too much, but in my case they do (min >>>> groupsize=2 max groupsize=1273) >>>> >>>> ttest <outcome>, by(treatvar) unequal -> t = -2.43 >>>> oneway <outcome> <treatvar> [aweight=cem_weight] -> F=4.06 >>>> >>>> both bartlett's test for equality of variance, a standard sdtest , >>>> and robvar suggest that I have unequal variance between groups. >>>> >>>> Suggestion on alternatives would be greatly appreciated > > * > * 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: means compairison with weights and unequal variance***From:*Nick Cox <n.j.cox@durham.ac.uk>

**References**:**Re: st: means compairison with weights and unequal variance***From:*"Ariel Linden, DrPH" <ariel.linden@gmail.com>

**Re: st: means compairison with weights and unequal variance***From:*Barbro Widerstedt <appoloniak@gmail.com>

**Re: st: means compairison with weights and unequal variance***From:*Nick Cox <njcoxstata@gmail.com>

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