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Thread: Unbalanced designs in PERMANOVA and Resampling Methods

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    Unbalanced designs in PERMANOVA and Resampling Methods




    Dear all,

    I am a new member and I have to say I am not sure if this is the right section for my question (I am working in ecology).

    I am dealing with unbalanced designs (I am comparing groups with different sample sizes) in PERMANOVA (Permutational Multivariate Analysis of Variance). The PERMANOVA runs fine, and results are consistent with patterns I can observe in a NMDS or PCoA plot. But I would like to rule out any possible effect of the difference in size between my samples on the significant result I obtain. This is because one of my sample is half the size of the others. So my question is : how could I do that ?

    I was thinking about a resampling method (bootstrapping) prior to the analysis, but then I realized there was an option in the software I am using (Primer Software) to calculate p-value using monte carlo approximation instead of permutations. I know that this option is meant to deal with small samples that don’t allow a sufficient number of permutations to calculate a p-value. In which case, monte carlo approximation is more suitable. But – please correct me if I am wrong - if this « monte carlo p value » is based on a resampling method shouldn’t it already rule out the possible effect of differences in sample sizes in the result I obtain ? In which case I could just use the monte carlo approximation in the test, and not having to go through bootstraping.

    I am really not familiar with resampling methods, so I might be completely wrong and any help would be much appreciated,

    Thanking you in advance


    Yser

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    Re: Unbalanced designs in PERMANOVA and Resampling Methods


    Sorry for the slow release of your post from moderation, and welcome to the forum!
    Matt aka CB | twitter.com/matthewmatix

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