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Date:         Sat, 12 Mar 2005 17:29:42 -0800
Reply-To:     LUCINDA M TEAR <lucindatear@msn.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         LUCINDA M TEAR <lucindatear@msn.com>
Subject:      Re: MANOVA with covariate
Comments: To: michael healy <healym@earthlink.net>
Content-Type: text/plain; charset="iso-8859-1"

I've mostly used covariates with the GLM procedure, but I imagine the concepts are similar with the MANOVA routine. Unless you add an interaction term between the covariate and a main effect, the model tests for a constant, LINEAR relationship between the covariate and the dependent variable across all the levels of any categorical factors. The slope of the covariate can be positive or negative, and if the effect is not linear, then you have not met the assumptions of the test and should be careful interpreting your p values (to check, do a scatter plot of time til death and self esteem and see if the relationship seems linear). It can be helpful to check for interactions between the covariate and the main effects (specified in the model in the same way that you would specify an interaction of categorical main effects) to see if the slope of the relationship between the covariate and the dependent variable is the same for each level of the factor. For example, your results indicate that self esteem increases (or decreases, depending on the slope of the covariate) with time to death at the same rate at all levels of intervention. If you add an interaction term to your model (between time to death and intervention), you will find out if the slope of the relationship is constant across all levels of intervention (the interaction will not be significant) or if it differs depending on level (the interaction will be significant). Adding an interaction makes the ANOVA an Analysis of Covariance. With MANOVA, I imagine the significant covariate means that there is significant linear relationship between time to death and self esteem, pain intensity, and # of physical symptoms AND that the slope of the relationship between time to death and each of those variables is the same. Printing out the parameter estimates will show you what the slope is...Plotting a scatter plot of time to death and each of your dependent variables and coding the data by level of intervention would help you see if the data for each level are intermixed with data for other levels or if the data for each level cluster together and apart from other levels.

Hope that makes sense....

Lucinda

----- Original Message ----- From: "michael healy" <healym@earthlink.net> Newsgroups: bit.listserv.spssx-l To: <SPSSX-L@LISTSERV.UGA.EDU> Sent: Friday, March 11, 2005 12:57 PM Subject: Re: MANOVA with covariate

> that older people are more likely to die. > > -----Original Message----- > From: Andrew Walsh <awalsh@uhnresearch.ca> > Sent: Mar 11, 2005 11:41 AM > To: SPSSX-L@LISTSERV.UGA.EDU > Subject: MANOVA with covariate > > hello all, > i am unsure of the proper interpretation of this analysis. i've run a > MANOVA with a single continuos variable as a covariate (time to death). > my factor has three levels (no intervention vs. late intervention vs. > early intervention) and i have 3 dependent variable (self esteem, pain > intensity, and # of physical symptoms). so my question is, what does a > significant F indicate when it is associated with the covariate for the > MANOVA? > thanks very much, > ~aw >


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