non-parametric two-way repeated measures anova
non-parametric two-way repeated measures anova
Is there a non-parametric alternative for repeated measures anova with more than one factor in jamovi?
Re: non-parametric two-way repeated measures anova
i'm not sure that such a thing exists ... so, no.
cheers
jonathon
cheers
jonathon
Re: non-parametric two-way repeated measures anova
Strictly speaking there is not - as you are dealing with 'ranked' as opposed to 'raw' scores then this would be problematic for the interaction of the two independent variables. I suppose you could analyse the main effects of each separate IV via the traditional non-parametric route, but you still wouldn't have the interaction effect. You could be sneaky and try to analyse what would be the post hoc tests non-parametrically (assuming the interaction would have been significant), but that would be frowned upon...
Re: non-parametric two-way repeated measures anova
Thank you both
I thought it must be possible to run a non-parametric Version of this Design but it seems like there is no alternative that can also handle an interaction between two repeated measures factors
I thought it must be possible to run a non-parametric Version of this Design but it seems like there is no alternative that can also handle an interaction between two repeated measures factors
Re: non-parametric two-way repeated measures anova
Would be great if there would be an option to use bootstrapping on repeated measures anova
Re: non-parametric two-way repeated measures anova
Though not with jamovi, you could do a permutation-test analog of a multifactor ANOVA.
Matrix of Cell Means in a 3 by 3 design:
A1B1, A2B1, A3B1
A1B2, A2B2, A3B2
A1B3, A2B3, A3B3
The test would address the question:
Given many random permutations of the data (i.e., random reassignments of data points to condition labels), and given a statistic such as 'the across-row variance of the across-column variances, how often is the magnitude of the obtained statistic at least as large as the same statistic computed on the un-permuted data?
Matrix of Cell Means in a 3 by 3 design:
A1B1, A2B1, A3B1
A1B2, A2B2, A3B2
A1B3, A2B3, A3B3
The test would address the question:
Given many random permutations of the data (i.e., random reassignments of data points to condition labels), and given a statistic such as 'the across-row variance of the across-column variances, how often is the magnitude of the obtained statistic at least as large as the same statistic computed on the un-permuted data?