multi vs univariate tests in GLM RM anova
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multi vs univariate tests in GLM RM anova
 
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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. bozena zdaniuk |
FW: multi vs univariate tests in GLM RM anova
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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?
bozena On Wed, Nov 30, 2011 at 9:03 PM, Zdaniuk, Bozena <[hidden email]> wrote:
From: Carlos Mora [[hidden email]]
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.
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Re: multi vs univariate tests in GLM RM anova
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Univariate repeated measures are based on somewhat different assumptions from multivariate tests. The latter assume multivariate normality and equal variance covariance matrices across groups. The former does not assume multivariate normality, just normality of the residuals, but do assume, in addition to equal variance covariance matrices, that the pooled variance covariance matrix is spherical. Another way of saying this is that all pair wise differences between the repeated observations have the same variance. Interestingly enough, the univariate repeated measures is typically more liberal than the multivariate tests, especially when the sphericity assumption is not met. The measure of this assumption is epsilon and there should be a test of epsilon less than zero. The smaller it is, the more the assumption is violated. There are adjustments to the degrees of freedom to reduce this effect but they do depend on how you estimate epsilon. The Huynh-Feldt correction is more liberal than the Geisser-Greenhouse estimate. Dr. Paul R. Swank, Children's Learning Institute Professor, Department of Pediatrics, Medical School Adjunct Professor, School of Public Health University of Texas Health Science Center-Houston From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Zdaniuk, Bozena 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? 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) From: Carlos Mora [[hidden email]] 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. |
Re: multi vs univariate tests in GLM RM anova
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In reply to this post by Zdaniuk, Bozena-3
From below, 1 Dec, "But in RM Anova, the univariate tests are not showing us effects separately for each measure. " ... "each measure"? - In RM Anova, the construction of the problem and hypothesis is such that you have only *one* measure, with multiple reports. Simple tests are done on the sum of the reports, or perhaps on polynomial contrasts if reports are ordered (like "time") and request them. Individual contrasts are unusual, and not very interpretable if there is a trend-line. From below, Nov 30, "The multivariate tests show all within-subject effects as significant but the univariate tests show two of those effects as non-significant." - I'm not sure which you are pointing to as "all within-subject effects", but you ought to be looking at the tests for the between-subject factors. The simple case of MANOVA is discriminant function; you do not ordinarily see or want to see a "within-subject" test as to whether the predictor variables in a DF are have equal means (which implies a matrix of paired tests, as followup) or have means equal to zero. A multivariate test is a test on the *pattern* among the variables. The pattern can differ as a consequence on one or more components, or (merely) the pattern among them, even with no variable being nominally significant. That is one reason why the general MANOVA is a good choice for an overall test performed with total agnosticism, but is a poor choice when you have prior knowledge about the expected results, or if you want to draw careful conclusions about any of the components. -- Rich Ulrich Date: Thu, 1 Dec 2011 04:27:40 +0000 From: [hidden email] Subject: FW: multi vs univariate tests in GLM RM anova To: [hidden email] 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?
bozena On Wed, Nov 30, 2011 at 9:03 PM, Zdaniuk, Bozena <[hidden email]> wrote:
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