Subject: | | Re: repeated measures in flexible factorial |
From: | | Colin Hawco <[log in to unmask]> |
Reply-To: | | [log in to unmask][log in to unmask]>:
> Hi Colin, > > (1) You need to add the subject factor as a main effect. Then it will > appear in your model. > > (2) The main effect of group is not a valid contrast in a repeated measures > design. This is due to the wrong degrees of freedom and the wrong error > term. The error term of this model is the within-subjects error. For the > main effect of group, you'd want the between-subjects error term, which is > not provided with the model. > > Best Regards, > Donald McLaren, PhD > > > > On Fri, Oct 5, 2018 at 4:46 PM Colin Hawco <[log in to unmask]> wrote: > >> Oh and I forgot part 2, my contrasts. Main effects are easy (1 1 -1 -1 >> or 1 -1 1 -) >> >> but for interaction, since it put the interaction terms in the model, >> I think it would be an F contrast of [0 0 0 0 1 -1 1 -1] >> Confirmation of this would make me feel a lot better, I've been as >> confident in f contrasts as I'd like as I so rarely make use of them! >> >> best, >> Colin >> >> Quoting [log in to unmask]: >> >> > Dear all, >> > >> > I'm sure this has been addressed before but my list search didn't >> > run up a clear answer (a reflection on my poor search skills than >> > the clarity of past answers, I am sure). >> > >> > I am running a repeated measures type ANOVA design, with a group >> > (between subject) by time/session (pre-post, within subject) design. >> > After some consideration, flexible factorial seemed the best way to >> > go. >> > >> > I set for main effects of time and session, as well as the >> > interaction. My design matrix is attached. >> > >> > I set independence for no for 'time', but not for group, while I >> > left variance unequal (after all we expect changes over time, so I >> > expect possible unequal variance). >> > >> > First I wanted to check if this seems OK. >> > >> > Second, I wanted to check if maybe we should model subject as an >> > additional factor? It seems to maybe be already embedded implicitly >> > in the flexible factorial. If I add this factor, but don't specify a >> > main effect, it doesn't appear in the design matrix, which I found a >> > bit surprising (it should still be modeled even if we don't contrast >> > it, for the effects on the Beta estimation). >> > >> > Thanks a lot, >> > Colin >>[log in to unmask] |
Date: | | Fri, 5 Oct 2018 15:44:55 -0500 |
Content-Type: | | text/plain |
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Oh and I forgot part 2, my contrasts. Main effects are easy (1 1 -1 -1
or 1 -1 1 -)
but for interaction, since it put the interaction terms in the model,
I think it would be an F contrast of [0 0 0 0 1 -1 1 -1]
Confirmation of this would make me feel a lot better, I've been as
confident in f contrasts as I'd like as I so rarely make use of them!
best,
Colin
Quoting [log in to unmask]:
> Dear all,
>
> I'm sure this has been addressed before but my list search didn't
> run up a clear answer (a reflection on my poor search skills than
> the clarity of past answers, I am sure).
>
> I am running a repeated measures type ANOVA design, with a group
> (between subject) by time/session (pre-post, within subject) design.
> After some consideration, flexible factorial seemed the best way to
> go.
>
> I set for main effects of time and session, as well as the
> interaction. My design matrix is attached.
>
> I set independence for no for 'time', but not for group, while I
> left variance unequal (after all we expect changes over time, so I
> expect possible unequal variance).
>
> First I wanted to check if this seems OK.
>
> Second, I wanted to check if maybe we should model subject as an
> additional factor? It seems to maybe be already embedded implicitly
> in the flexible factorial. If I add this factor, but don't specify a
> main effect, it doesn't appear in the design matrix, which I found a
> bit surprising (it should still be modeled even if we don't contrast
> it, for the effects on the Beta estimation).
>
> Thanks a lot,
> Colin
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