EJ
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- EJ
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Comments
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I'll ask our expert! E.J.
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Dear C, You are interpreting the output correctly. The discrepancy to the frequentist result worries me a little and I'd like to get to the bottom of it. Did you use our latest ANOVA implementation? (see https://jasp-stats.org/2022/07/29/bayesian-re…
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Dear C, >Can I also rely on the Bayesian ANOVA results if my data is ordinal? The same issues apply as for frequentist inference. >I have my DV labelled as ordinal in JASP - am I right that this is not considered when I perform a Bayesian ANOV…
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Probably. I'll make sure we add this information to the help files. E.J.
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Hello Julia, Yes you've understood that correctly. Yes it would make sense to set that to the same value (I'd use the scale for the continuous covariates) If you include terms in the null model, these terms are common to the null and the alternative…
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I'd go over https://psyarxiv.com/s56mk Ultimately this is about the prior plausibility of the hypotheses. E.J.
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Ah I see. I am sure it is the non-parametric bootstrap. It seems to be you can bootstrap just about anything. If you want to check you could set the number of bootstrap samples to something really low (e.g., 10) and see whether the results change wh…
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Where exactly do you see the bootstrap option? And this is not clarified in the help file? E.J.
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well, it is always more elegant to account for the entire hierarchical structure instead of averaging, but averaging does give you some robustness, so I'm fine with averaging here
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We offer a bunch of tests, but not this one I think. Would be a good feature request for our GitHub page! E.J.
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I'll talk to the team. We have not really done much work specifically on the analysis on panel data, but we are in the process of expanding our functionality with time series models so this seems a logical development. Cheers, E.J.
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We don't offer power analyses just yet, but it is on its way! E.J.
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Yes the same applies for Bayesian statistics Reporting both seems a good idea?!
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This is a problem for our programming team, which you can reach through our GitHub page. For details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/
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There does not appear to be an update on this, but you could of course use GitHub to express your enthusiasm for adding this functionality
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Well, those options do seem to be there. Did you find anything missing? E.J.
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That's a little strange, I'll forward this to our expert. E.J.
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Hi KV, In these kind of situations I always wonder: if levels 2-4 are not supposed to differ, why was the experiment set up this way? Anyway, yes, a rough method is just to average within levels 2-4. There are more elegant (multi-level) procedures a…
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You can't find it online?! EJ.Wagenmakers@gmail.com
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The DV in logistic regression is usually a binary variable, right? JASP offers several resisual plots.
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Hi BJ89, You say "I have been told by a supervisor that BMA is never an appropriate method to use because it is 'step-wise'" I am not sure what you mean here, because BMA is a single step method where all models are fit to the data simulta…
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Hi Dimitri, Without knowing the details for this analysis, it is generally true that adding more components *always impoves the model's best-fit*. For instance, adding predictors in a regression can only increase the proportion of variance explaine…
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No worries! If you still want me to take a look you could send the data to my personal email address (but don't feel pressured) E.J.
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Dear Alejandro, That sounds like a great suggestion. Could you add it as a feature request to our GitHub page please? (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) Cheers, E.J.
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OK. So first I confirm that in BayesFactor yields BF01 = 3.3 (approximately) #R code: library(BayesFactor) proportionBF(52,173,1/3) Second, the default Bayesian test in JASP gives BF01 = 7.5 (approximately). See screenshot. https://forum.cogsci.nl/u…
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I'll look into it a bit more. E.J.
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Hi BJ89, We discuss the impact of model priors on inclusion BFs here: https://journals.sagepub.com/doi/full/10.1177/25152459211031256#appendix I agree it is counterintuitive. If you send me the data I can have a look E.J.
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Hi Lewend, Well, these are different models. JASP uses the test proposed by Jeffreys -- under H1, theta gets a uniform prior from 0 to 1. You can change that distribution by adjusting the a and b parameters of the beta distribution. In BayesFactor, …
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Hi Mathieu Yes, we now have a graph editor that allows the axes to be adjusted. It does not work for all plots, but it works for most. E.J.
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Ah I see. Was this a GitHub request already? E.J.