Date: Thu, 25 Jan 2001 10:08:19 -0800
Reply-To: "Dennis G. Fisher" <dfisher@CSULB.EDU>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: "Dennis G. Fisher" <dfisher@CSULB.EDU>
Subject: Re: Proc GLM
Content-Type: text/plain; charset=us-ascii
I have to weigh in on this one. Usually I would agree that ruining a perfectly
good continuous variable by dichotomizing it is not a good thing to do and I
once gave such advice to a grad student. It turned out that I was wrong. The
variable was birthweight. This actually turned out to be a dichotomous
variable, which is something I did not know at the time. Infants can be
classified into low birth weight and non low birthweight. Low birth weight is a
proxy (or perhaps an indicator) that there were problems with the pregnancy. So
non-low birthweight infants mean that the indicators of lbw problems were not
present. It does not mean that infants who are very heavy are somehow protected
against these problems. In the case of this grad student, the infants should
have been classified into low birth weight and non low birthweight. Weight
should not have been treated as a continuous variable. You have to understand
the meaning of the variable before giving an opinion about the analysis. So I
guess I agree with Dr. Kruse.
Just my 2 cents.
Dennis Fisher
Dr Olaf Kruse wrote:
> cchang7814@my-deja.com wrote:
>
> > A colleagues of mine suggested to dichotomize "age" as categorical
> > variable. Do you think that will help?
>
> Paige Miller replied
>
> >>In regression, I have yet to see an example where taking a continuous
> >>variable and making it a class variable has helped. Nor am I aware of
> >>a logical reason why it might be a good thing to do.
>
> IMHO it depends on the _true_ relationship between the variables. If you use
> age as a continous variable for explaining
> something that is related to the _true_ relationship "after/before
> retirement", you are better off by dividing
> age into two classes "before/after 65" (official retirement-age in Germany).
>
> Treating a variable as continous in a linear model can be a very restrictive
> assumption, if the relationship is not linear.
> Proper dichotomizing can help you (among other techniques) detect non-linear
> relationships and outiers.
> Both ways have its pros an cons.
>
> Cheers, Olaf
>
> +-----------------------------------------------------+
> Dr. Olaf Kruse
> VST -Gesellschaft fuer Versicherungsstatistik
> Roscherstr. 10
> 30161 Hannover/FRGermany
>
> mail: Olaf.kruse@vst-gmbh.de
> phone:++49-511-339 599 21
> fax: ++49-511-388 57 13
> +-----------------------------------------------------+
--
Dennis G. Fisher, Ph.D.
Director
Center for Behavioral Research and Services
1090 Atlantic Avenue
Long Beach, CA 90813
562-495-2330
562-983-1421 fax
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