Date: Tue, 6 Jun 2006 08:18:51 -0400
Reply-To: Jim Groeneveld <jim2stat@YAHOO.CO.UK>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Jim Groeneveld <jim2stat@YAHOO.CO.UK>
Subject: Re: QR: Bonferroni correction in multiple linear regression?
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When performing anova or multiple regrassion one never should do any
adjustment to alpha levels on emerging effects. These analyses test for
effects of some independent continuous or categorical variable(s) on some
dependent variable(s) as a whole, thus without pointing to significant
differences between pairs of groups in case of three or more groups. This
actually is a single test, not a series of multiple tests. You may have been
far too conservative with your alpha levels in this case.
Only if one performs post hoc analysis or a priori tests between pairs of
groups (e.g. t-tests), which involve more than one separate (multiple) tests
some alpha adjustment applies because of higher probabilities on
coincidentally significant results. Bonferroni is quite common, quite
conservative too, your effects have to be strong to prove significant. Other
adjustment methods are available of which the one proposed by Keppel is
quite liberal. Keppel only applies a Bonferroni-like adjustment in case of
circular dependence of sets of tests between groups.
Search with google on [alpha bonferroni keppel partitioning multiple].
Regards - Jim.
Jim Groeneveld, Netherlands
Statistician, SAS consultant
On Tue, 6 Jun 2006 12:37:23 +0200, adel F. <adel_tangi@YAHOO.FR> wrote:
> in one way anova , we usually use the Bonferroni correction , if we have
a categorical variable X, of more than 3 categories, for example 5, in
order to test the equality of means of the Y variable.
> If instead of anova, I use the multiple linear regression Y= X, do I need
to do the Bonferroni correction here also?
> Many thanks
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