Yes, it's working! Sorry then, next time I'm going to update before asking

Thanks!

Marie

Statistics: Posted by mdelacre — Fri Jul 19, 2019 12:33 pm

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i feel like using a square root with non-parametric data is problematic. so with parametric data, there's some mathematical foundation for using squares, and summing them, but i think that's all possible because we know that two is twice one, etc.

but with non-parametric data, we don't know that two isn't ten times one, or a hundred times one, so i don't feel like taking square roots would be a correct approach ... (but you're possibly getting the impression that i only grasp this stuff intuitively). i probably need ravi to chime in.

could you point us to an article which explains it?

with thanks

jonathon

Statistics: Posted by jonathon — Fri Jul 19, 2019 2:03 am

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i think we found and fixed that issue a while ago. update to the latest version and let us know if it's still a problem.

sorry for the inconvenience!

jonathon

Statistics: Posted by jonathon — Fri Jul 19, 2019 1:58 am

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I don't know if it's a bug or if I'm doing anything wrong but I've tried many things and I could never display the results. When I uncheck the DSCF pairwise comparisons, everything's fine. I use the 1.0.1.0 version. If ever it is useful, I'm attaching the .omv file.

Thanks a lot for your help.

Marie

Statistics: Posted by mdelacre — Thu Jul 18, 2019 6:24 pm

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I have a query about the choice of effect size for running Mann-Whitney or Wilcoxon Signed Ranks analyses - both default to reporting Cohen's d (presumably as they are sub-options in the t-test analyses). However, I was always taught to use r - as in 'Z / sqrt #data points' in the instance of these non-parametric equivalents and this is what we've previously taught students to use when running SPSS.

Is it possible to have this calculated instead of or as well as Cohen's d when selecting the non-parametric option?

Cheers - and keep up the good work!

Statistics: Posted by Bobafett — Thu Jul 18, 2019 12:58 pm

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you *probably* don't want years as your dependent variable. depending on your research question, you probably want the number of trucks sold to be your dependent variable, and year and colour to be your explanatory variables.

this is probably a candidate for contingency tables, rather than linear regression.

cheers

jonathon

Statistics: Posted by jonathon — Wed Jul 17, 2019 12:35 am

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i think we're mostly opposed to stepwise regression - we don't really find it to be a principled approach, and think model selection using things like BIC, AIC, etc. is a better approach. so it's not something we've implemented (although, its certainly something which could be provided by a module).

wrt semi-partial correlations, i'm not crystal clear on that, so i'll leave that for ravi to chime in.

cheers

jonathon

Statistics: Posted by jonathon — Mon Jul 15, 2019 1:37 am

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Or perhaps, is there a way to generate the semi-partial correlations in Jamovi?

Thank you.

Statistics: Posted by marshall — Sat Jul 13, 2019 10:47 am

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Statistics: Posted by mcfanda@gmail.com — Tue Jul 09, 2019 1:20 pm

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Statistics: Posted by mcfanda@gmail.com — Tue Jul 09, 2019 1:12 pm

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Statistics: Posted by mcfanda@gmail.com — Tue Jul 09, 2019 1:10 pm

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i don't think this is possible in jamovi at this time.

i know marcello's been thinking of implementing this.

cheers

jonathon

Statistics: Posted by jonathon — Tue Jul 09, 2019 12:50 am

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Statistics: Posted by pennefather — Mon Jul 08, 2019 9:32 pm

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