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Dec
7
comment Why does a mixed design using R's aov() need the between subject factors specified more than once?
Good to have it checked by an expert, thanks! Strange that a couple of web sites have this in their examples.
Dec
7
accepted Why does a mixed design using R's aov() need the between subject factors specified more than once?
Dec
7
comment Why does a mixed design using R's aov() need the between subject factors specified more than once?
Thanks, fixed it above.
Dec
7
revised Why does a mixed design using R's aov() need the between subject factors specified more than once?
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Dec
6
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Dec
6
asked Why does a mixed design using R's aov() need the between subject factors specified more than once?
Sep
21
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Sep
2
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Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
let us continue this discussion in chat
Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
Good point, I hadn't realised there were only four for participants, and I've not looked into how they are calculated yet. However, I don't understand what you mean by strata. How would you look at between-subject error? You mean the residuals from the group means without correcting for individual error? Like in a between-subjects design? My (basic) statistics textbook assures me that these don't have to be normally distributed in a repeated measures design. Perhaps it's enough to look at the within-subject error. We should move this to chat.
Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
Yes, you are right, they are both assumed to be random. Surely one can then look at my.aov$"participant"$residuals and my.aov$"participant:group"$residuals to check the between and within subject errors respectively?
Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
You mean after correcting for individual error? These are symmetrical around zero, so you could not say either 'first' or 'second' measurements were troublesome. Essentially with two time points there is only one within-subject error per participant. Or do you mean finding which participant might be troublesome? In which case you can do that from the 5 residuals from the model: each one corresponds to the within-subject error for each participant. If one is particularly high it could be an outlier.
Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
With regards to why we apply the model in this way to our data, I think it's because we can specify our predictions by adjusting the transformation matrix, e.g. to predict for a quadratic or cubic effect of time. But I've certainly never done that in an analysis before.
Sep
2
comment Is SPSS giving the wrong residuals for a repeated measures design?
As far as I know, these are the ones people should be interested in for a repeated-measures design, because they are the residuals from the model that we are applying to our data. The df from the anova = 4, so we would expect 5 residuals (only one variable is being modelled in this case, that represents the difference between the two groups, i.e. the within-subjects effect). I am sure the values are somehow mathematically related to the ones you calculated above.
Sep
2
answered Is SPSS giving the wrong residuals for a repeated measures design?
Sep
1
comment How is an ANOVA calculated for a repeated measures design?
Great answer, that's really helped me finally understand what an ANOVA is doing! Thanks also for the book reference!
Sep
1
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Sep
1
accepted How is an ANOVA calculated for a repeated measures design?
Sep
1
comment How is an ANOVA calculated for a repeated measures design?
Ah that's helpful, ok, so from the model lm(value ~ factor1*factor2*participant, DFlong), how are the sums of squares actually calculated? i.e. what is anova() doing?
Sep
1
asked How is an ANOVA calculated for a repeated measures design?