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From | John Litfiba <cariboupad@gmx.fr> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: too good to be true : lr test in mlogit? |
Date | Fri, 13 May 2011 12:58:14 +0200 |
Got the paper, Thank you again On 13 May 2011 12:04, Maarten Buis <maartenlbuis@gmail.com> wrote: > On Fri, May 13, 2011 at 10:45 AM, John Litfiba wrote: >> I would be definitively interested if by chance you have in mind a >> paper that discuss the large sample side effects on P-values that you >> mention > > The argument is straightforward: with larger sample sizes we are able > to detect smaller and smaller effects. If we included a variable in > our model than it is extremely implausible that the effect of that > variable is exactly zero (i.e. the null hypothesis is true). If we > reject the null hypothesis is rejected that the effect was so small > that the dataset was not large enough to detect it. So by getting ever > larger samples we will start to find ever more effect, but they will > be so small that they are substantively irrelevant (even though they > are statistically "significant"). > > One discussion of this is: > Raftery, Adrian E. 1995. "Bayesian Model Selection in Social > Research." Sociological Methodology, 25: 111-163. > Also see the responses to this article that appeared in the same issue. > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > > http://www.maartenbuis.nl > -------------------------- > * > * 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/