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Re: st: too good to be true : lr test in mlogit?


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
> --------------------------
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