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Hi Colin, Yes. The interaction contrast is still valid [0 0 0 0 1 -1 -1 1]. The only difference between the F- and t-contrasts when there is only 1 row is that the F-test does not have a direction associated with it. The F-value is t^2. The significance of the F-test is the same as the two-sided significance of the t-test. You can determine if your contrast is testing what you expect by starting with the null hypothesis and using the null hypothesis to form the contrast. Multi-row F-contrasts are needed when you have multiple equalities in your contrast (e.g. H0: A=B=C). Best Regards, Donald McLaren, PhD On Mon, Oct 8, 2018 at 3:27 PM <[log in to unmask]> wrote: > Thanks Donald. > > I don't think the main effect of group is very sensible anyways, since > it is collapsing between time points and therefor of little interest. > > The interaction ioss till valid though right? > > We were planning a contrast os 0 0 0 0 1 -1 - 1 (the 4 interaction > terms, skipping the main effects terms) as an F contrast. I'm not 100% > sue that is right though. I'm not very experienced with F contrasts. > > I'd appreciate a second opinion : ) > > Colin > > > Quoting "MCLAREN, Donald" <[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 > >> > > > >