Date: Wed, 3 Oct 2007 07:44:19 -0700
Reply-To: "Ornelas, Fermin" <FerminOrnelas@azdes.gov>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Ornelas, Fermin" <FerminOrnelas@azdes.gov>
Subject: Re: Regression Analysis
Content-Type: TEXT/plain; charset="iso-8859-1"
Whenever you have dummy variables you will always have one dummy less fit into the model. As Hector already pointed out you want to avoid perfect correlation between two predictors. The effect on the missing dummy is collapsed into the constant coefficient.
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Björn Türoque
Sent: Wednesday, October 03, 2007 6:31 AM
Subject: Re: Regression Analysis
This may sound like a dumb question, but did you happen to make two gender
variables one for male (values 1 and 0) and one for female (values 1 and
))? Putting both of them in will cause problems, but I don't know if it will
cause the problem you are talking about. For your regression you should only
use one variable for gender.
I only bring this possiblity up because one very simple mistake I often see
when people first run regression with recoded dummy variables is that they
place all of the dummy variables into the equation, when you need to leave
at least one out.
On 10/2/07, Matt Donley <email@example.com> wrote:
> Hello all,
> My name is Matt, I am a third year psychology student at Monash University
> in Melbourne, Australia.
> My group is conducting a research project into the relationship between
> alcohol consumption and sex-related alcohol expectancies, with gender and
> age as moderating factors. In order to obtain results, we are running a
> hierarchical multiple regression using SPSS v15.0 but have encountered a
> After centering relevant variables including age and recoding gender using
> dummy variables (0,1) and performing interaction terms, we ran the
> regression analysis.
> First level: sexual expectancies
> Second level: age and gender
> Third level: interaction terms
> We found significant results for enhancement (a sexual expectancy) and
> genderXenhancement. We are now calculating an interaction graph and
> require unstandardised correlation coefficients but for some reason SPSS
> has excluded gender.
> Can anyone please suggest possible solutions as to how we would go about
> getting this data from the regression analysis? We have tried recoding the
> gender data as suggested by our tutor but we still do not get the data we
> need to produce the interaction graph. Any help would be greatly
> appreciated as we are really sturggling and running out of time before we
> have to hand it in.
> Thank-you in advance,
375 Hudson Street
New York, NY 10014-3657
Some people are just born to rock!
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