jamovi

In the (Bayesian) paper: https://pubmed.ncbi.nlm.nih.gov/35357978/ and also the famous (frequentist) Bar paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881361/ we want to specify all random slopes in so far as the data support the hypothesis that this variance exists. The recommendation given ...

A really minor point, but just in case others stumble on this thread they might be confused by a typo in the original question: In particular, it appears that the p values may (but not necessarily) be inflated when the model includes random slopes but not random intercepts I think you meant to say i...

Thanks Simonmoon!

Actually this problem was solved (see here: https://forum.cogsci.nl/discussion/8413 ... ion#latest)

We have a bipolar scale that asks do you agree with A more or b more It happened to be coded this way (1) completely agree with A <--> (7) completely agree with B So (4) is indifference It should have been coded this way: (-3) completely agree with A <--> (3) completely agree with B So (0 is indiffe...

Oh - also found a bug. If I set scale back to "None", duplicate the analysis and change the IV to something new it will still say the scale is set to "None" but it will be centered again. You need to click on any of the other ones and then back to "None" again.

Oh it appears to be the Scaling - I didn't realise that would so drastically change the p values. The interpretation of the two different results is drastic - the Explanatory Variable explains all the variance or it doesn't. Not sure how to interpret.

There is a significant difference in response between two conditions. We want to see if this difference goes away by accounting for another variable (i.e. to see whether it is fully explained by that variable). So we have Response ~ Condition * Explanatory Variable Super simple. If I run a linear re...

That doesn't seem quite right (unless I doing it wrong).

My data could look like this: And Friedman's won't let me test between groups 1 and 2.

Putting this really simply, I will have data from two different groups of participants where they rank order the "moral worth" of a set of non-human animals. I want to be able to test whether the rank ordering between the two groups is significantly different. Is there an appropriate test ...

Yes, I would agree with your reasoning. If the "group" of data points for "good payouts" is much smaller than the others, when you code the variable as continuous its effect will be "driver" mostly by bad and medium payout, because they represent when the majority of t...