Hi Darren,
Indeed, that's not exactly what I am saying.
(2) is the right model, but only provided the group effect is real. I think
it's the wrong model to assess whether that is the case, as the residual
variance against which such an effect should be tested is removed since it
is modeled by s_i(j). (It would be the same as including every individual
(within-condition) item mean in the model when trying to assess the validity
of a condition effect.) Therefore, to assess whether there is a group effect
(1) is the correct model as it leaves variability between subjects in the
residuals.
Moreover, (2) is correct (and indeed more sensitive, as Jan explained) when
you are interested in either within-subject effects or group-by-condition
interactions (which is often the case) as these effects are tested against
within-subject variance.
My claim was only about testing for main effects of group, which are
between-subject effects.
Best,
Laura
-----Original Message-----
From: Darren Gitelman [mailto:[log in to unmask]]
Sent: Monday, 06 April, 2009 00:21
To: Laura Menenti
Cc: [log in to unmask]
Subject: Re: [SPM] flexible factorial - main effect of subject factor
Laura:
So are you suggesting that if modeling a repeated measures design with
a group (between) and a condition (within) factor the equation (and by
implication the design) should be
(1) y_ijk = g_j + c_k + gc_jk + e_ijk
and not
(2) y_ijk = s_i(j) + g_j + c_k + gc_jk + e_ijk ?
As far as I can tell looking at books on mixed model designs they say
the 2nd equation is the correct one for a repeated measures mixed
model design. I think the 1st equation would be correct for a standard
factorial ANOVA if one assumes independence between all the measures,
but I may be misunderstanding you or misunderstanding these designs.
-----
Darren Gitelman
On Sat, Apr 4, 2009 at 2:18 PM, Laura Menenti
<[log in to unmask]> wrote:
> d
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