bivariate and continuous) there are three independent variables: sample age (this is a between-subject factor, as each sample is only measured at one age), and amplitude and frequency (these are within-subject factors, as each sample is measured at all (3 levels) amplitudes and all (16 levels) frequencies. In my experiment, the dependent variable is complex (i.e. Can I assess the effect size of my within-subject factors separately from my between-subject factors? Hello I’m hoping for some clarification on MANOVA when there are both within-subject and between-subject factors. I will shortly change this to Iterations for consistency. I originally used “sum count” since the iteration is really an infinite sum, but after adding a certain number of terms, the sum doesn’t change enough to continue adding more terms. Pillai’s V is defined at MANOVA Basic ConceptsĤ. Namely, if ttype = 1 then effect size = partial eta-square if ttype = 1 then effect size = eta-square if ttype = 3 then effect size = Pillai’s V. ttype is defined at MANOVA Power and Sample Size. The default precision values should be sufficient for all your needs.ģ. prec) is reached, at which point the iteration stops even if the maximum number of iteration has not been reached. iter) is reached or until the desired level of precision (i.e. 1.276 is more precise than 1.3) Essentially in an iterative calculation, the iterations continue until either the maximum number of iterations (i.e. You can think of of it as the number of significant decimals in the result (e.g.

prec is a parameter that specifies the desired level of precision. However, at some point additional iterations don’t add enough precision to make the slower calculation time worthwhile. In general, the more iterations, the more accurate (i.e. Note that the more iterations the slower the calculations, and so iter = 1,000 is chosen since it is high enough to get an accurate answer but low enough so that the calculations are fast.Ģ. If you have any doubt, simply increase the value of iter to some higher number and see whether the output changes in the unlikely event that it does change, then you should experiment by using higher and higher values until no change is detected. This should be more than enough for anything you are apt to do and so you can keep the default. iter = the number of iterations used where 1000 is the default. The more iterations, the more accurate the answer (although at some point additional iterations may not generate any further accuracy). The values from this distribution can’t be obtained using a simple formula instead it is calculated using an iterative approach. The power calculation uses the noncentral F distribution.

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