Date: Mon, 24 Jan 2011 12:58:32 -0800
Reply-To: Bruce Weaver <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Bruce Weaver <email@example.com>
Subject: Re: problem with casewise list and classification plot in
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You have other more serious problems to deal with here. The classification
table in your output gives this frequency distribution for your outcome
732 -- No Combined RMC
21 -- Combined RMC
So you are severely over-fitting the model. According to the rule of thumb
given in Frank Harrell's book (Regression Modeling Strategies, Springer),
you should have at least 15-20 events per model parameter. An "event" is
defined as the outcome variable category with the lower frequency count. So
you have 21 events. Therefore, you can only really include one (single
degree of freedom) explanatory variable. (Bear in mind that the constant
counts as a parameter.)
For more information on over-fitting, see Mike Babyak's nice readable
> I am using binary logistic regression to test this model:
> logit(ρ1) = α + β1(INDDIR) + β2(INDCHAIR) + β3(BOARDSIZE) + β4(DIRSHIP) +
> β5(MEETING) + β6(EXPERT) + β7(INSTI) + β8(DEBT) + β9(LnSIZE) + β10(BIG4)
> I have attached with the output of 2nd run. I have some doubt on the
> casewise list (outliers). The first time I run the analysis, there were 44
> outliers. I deleted all these 44 cases. Then, I re-run the analysis.
> Again, 11 outliers were appeared. I deleted all these 11 cases then
> re-run. Again and again, 6 outliers were found!
> So, should I continuously delete all outliers or it would be a never
> ending story?
> Also, does the classification plot looks weird? I not sure how to
> interpret it.
> Please help. Thanks.
"When all else fails, RTFM."
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