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Date:   Thu, 13 Nov 2003 13:06:31 -0500
Reply-To:   Mark Davenport <madavenp@OFFICE.UNCG.EDU>
Sender:   "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:   Mark Davenport <madavenp@OFFICE.UNCG.EDU>
Subject:   Re: Eta Squared & Tests for Curvilinear Relationships
Comments:   To: Colin.Valdiserri@calphalon.com
Content-Type:   text/plain; charset=US-ASCII

This looks to be a multivariate problem (in the sense of MANOVA) not a multiple regression problem. Multiple regression is a single D.V. procedure. Are you intersted in putting together 7 optimal prediction models and comparing them? If so, I don't see how you are going to do this.

Can you elaborate?

Mark

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Mark A. Davenport Ph.D. Asst to the Vice Chancellor for Student Affairs/Research and Evaluation The University of North Carolina at Greensboro 149 Mossman Bldg. Greensboro, NC 27402-6170 336.334.5099 madavenp@office.uncg.edu

'An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem' -- J. W. Tukey

>>> "Valdiserri, Colin" <Colin.Valdiserri@calphalon.com> 11/13/2003 10:18:15 AM >>> Hello Everyone,

I have 3 independent variables and 7 dependent variables. I have been using in correlational analysis and multiple regression analysis look at different prediction models. I am trying to examine the linearity of the variables and was instructed to turn to the Eta Squared statistic. I ahve been runining one-way ANOVA's and dividing the sum of squares between by the sum of squares total to get the Eta Squared, then comparing that to the R Squared in my models.

I also was instructed to get the mean for each DV at each category of each IV and plot them to assess linearity. This all seems like quite a lot to do by hand and I figure that SPSS has to have a way to assess curvilinearity.

Any help is greatly appreciated.

Colin M. Valdiserri, M.A. Consumer Scientist The Calphalon Group (419) 662-2378 - Office (419) 661-0223 - Fax colin.valdiserri@calphalon.com

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