Please accept this correction, "So, if I am not mistaken, would want to have
two within-subjects factors representing the 2 types and 2 styles with
degrees of freedom (1,14)."  I got the degrees of freedom wrong.

---------------------------------------------------------------
Jeff Dang, MPH
Statistician
UCLA Cousins Center for Psychoneuroimmunology
(Affiliated with the Neuropsychiatric Institute and
Department of Psychiatry and Biobehavioral Sciences)
300 UCLA Medical Plaza, Room 3148
Los Angeles, CA 90095-7057
Tel: 310-267-4389
Fax: 310-794-9247
E-mail: [EMAIL PROTECTED] 
Web: http://www.cousinspni.org/

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-----Original Message-----
From: Dang, Jeff 
Sent: Thursday, January 29, 2004 12:47 PM
To: '[EMAIL PROTECTED]'
Subject: RE: [edstat] one way ANOVA or repeated measures ANOVA

It's funny how such a simple problem becomes somewhat tricky....

Let's clarify the shirt problem because I think there may be some confusion.
There are n=8 subjects, two types, and two styles.  So, if I am not
mistaken, for the one-way repeated measures ANOVA you would want to have two
within-subjects factors representing the 2 types and 2 styles with degrees
of freedom (2,14). 

You could also separate the independent variables up as James suggests into
1 within-subjects factor with degrees of freedom (3, 21).  But this would
not account for the fact that the "sizes" are nested within "shirts".  You
should let your hypotheses drive your analytical decision.

Jeff Dang

-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 
Sent: Thursday, January 29, 2004 9:11 AM
To: [EMAIL PROTECTED]
Subject: Re: [edstat] one way ANOVA or repeated measures ANOVA

this is a one-way repeated measures ANOVA.  total DF=31, partitioned
into 7 between subjects and 24 within subjects.  the 24 are further
broken down into 3 for types of shirts and 21 for error (subjects by
shirts interaction).  the "test" uses the F with 3 and 21 degrees of
freedom.  the test statistic is the ratio of the mean square for
shirts to the mean square error.  if the F is significant, you can do
a post hoc test to look at whether the types differ across sizes or
the sizes differ across both types.

have fun




[EMAIL PROTECTED] (Archana) wrote in message
news:<[EMAIL PROTECTED]>...
> Hi,
> 
> Iam textile engineering looking for some statistical advice.
> I have the following test garments (total 4 types)-
> 
> Type A - medium and large
> Type B - medium and large
> 
> The same 8 subjects wore the shirts and perfomred some physical
> activity. Their heart rate, skin and core temperature etc were
> monitored. I want to find out if the heart rate or the other
> parameters are significant for the 4 types of shirt. What do i follow?
> One way anova or repeated measures design and why?
> your answers would be of great help to me.
> 
> Thanks
.
.
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