Date: Thu, 1 Dec 2011 04:27:40 +0000
Reply-To: "Zdaniuk, Bozena" <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Zdaniuk, Bozena" <email@example.com>
Subject: FW: multi vs univariate tests in GLM RM anova
hello, i received a couple answers to my post which made me realize i may not have explained my issue well. I understand the difference between the multivariate and univariate tests in the MANOVA (multiple dependent variables) but I am not sure i understand it in the RM Anova (the same DV measured multiple times). In MANOVA, the multivariate tests tell us if there are effects across all DVs. Then the univariate tests show us the effects separately for each DV. But in RM Anova, the univariate tests are not showing us effects separately for each measure. We still have exactly the same set of effects that include the within-subject factor as we have in the multivariate tests. So, how are they different?
On Wed, Nov 30, 2011 at 9:03 PM, Zdaniuk, Bozena <firstname.lastname@example.org<mailto:email@example.com>> wrote:
Hello, i am running one within, two between subject RM ANOVA using GLM. The multivariate tests show all within-subject effects as significant but the univariate tests show two of those effects as non-significant. My intuition is to use the univariate tests but I don't know exactly why (and whether it is what I should do). Why would there be such a difference between the two types of tests? My N is very large (70, 000)
thanks in advance for help.
From: Carlos Mora [firstname.lastname@example.org]
Sent: Wednesday, November 30, 2011 6:36 PM
To: Zdaniuk, Bozena
Subject: Re: multi vs univariate tests in GLM RM anova
The null hypothesis of the multivariate test is that all means are equal. If two of those are not equal, the test yields significant results at he chosen level of confidence. One way of peeling down the onion of pairwise difference is through contrasts. If you are going to use univariate tests, then you should extract random samples from your large data set and run the test on a fresh subsample.