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Subject:

Factorial Design w/ Partitioned Error, are DFs incorrect?

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Date:

Tue, 3 Nov 2009 21:28:38 +0000

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 ```Dear SPMers, I have recently run a study in which I would like to analyse the data as a factorial 3x2 repeated measures ANOVA (using the two-stage partitioned error approach), but have a question about the analysis of the main effects and their interaction at the group level. For the 3 level factor there are 2 differential effects (similarly, for the interaction there are two differential effects) that must be modelled at the group level. According to Rik Henson's SPM paper, you create a one-way ANOVA with n levels (n is the number of differential effects / columns in the design matrix, for my example that's two) and assess the overall effect with the F-contrast 'eye(n)'. Now this works OK, except that the degrees of freedom seem to be severely inflated for any effect that has more than 1 differential effect. For example, with my 19 subjects, one would expect the DF to be [2,18] when assessing a 3-level factor, but the two-stage partitioned error approach gives a DF of [2,36], and hence an inflated p value. Is this a problem? How does one work around this? The problem would seem to be even worse when using a pooled error ANOVA since then your DF would be inflated by a factor equal to the number of conditions you have! Am I missing something? Thank you in advance for any insights! Conor Wild ```