jamovi

Oh. OK. So it looks like the other value of supp (i.e., "VC") is excluded completely from the interaction post-hoc test. So probably best to just remove the "interaction" post-hoc test table since, for whatever reason, it doesn't actually work right.

I would also have doubts. For example, in a Walrus (bootstrap) robust ANOVA on the Tooth Growth Data, I think there should be 15 post-hoc comparison. But the output produces only 3.

I found a non-technical work-around: I simply include a 'table of contents' telling the reader wear to look in the numbered-but-out-of-order analyses. For example . . . RESULTS Contents Response Time General Descriptives for RT (#5) ANOVA of RT (#3) Rj planned comparisons for ANOVA of RT (#6) Accura...

I believe it's the residuals for the entire model that need no be normally distributed. https://statistics.laerd.com/statistical-guides/independent-t-test-statistical-guide.php#:~:text=Assumption%20of%20normality%20of%20the,give%20you%20the%20same%20result. Also, since the Shapiro-Wilk test is a sig...

What I've found is that many institutional computers have app blockers that may allow core aspects of jamovi to run while blocking other aspects. I've found that this blocking is especially likely to affect modules. Moreover, I've found seolmatrix to be more affected than any other jamovi module I u...

Oh. Sorry. However, I think those comparative measures are only computable in situation where the analysis can be conceptualized as comparing two things (to proportions, two ratios, etc.). Thus, they only work for 2 by 2 tables: https://cran.r-project.org/web/packages/chisquare/chisquare.pdf I've se...

Hi. jamovi's chi square contingency analyses aren't limited to 2 by 2. 3 by 4 works just fine.

@jonathan Is there supposed to be a jamovi Version 2.5.x for Windows, or is the new version only for non-Windows platforms?

The quantified parameter needs to be a 'close-enough' approximation to interval/ratio. Most analysts think that the kind of scores you have are close enough. You can adjust for multiple comparisons. You'll need to use something like jamovi's Rj module (or R). See the example code, below: # This code...

Within the context of traditional, well-known statistical analyses, I don't think a "mix of paired and unpaired data" is analyzable in any streamlined way. You have three groups rather than four, and you need to do a set of t tests--most of which will be independent-samples t tests, and on...