andersony3k
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Distinct from the issue of statistical power is the question of whether you can demonstrate that you didn't capitalize on chance by halting data collection as soon as you found a sufficiently-extreme Bayes factor to warrant a clear inferential stati…
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Hi Dexterama. I think you should not expect the ANOVA results to be compatible with the ANCOVA results, since the two types of analyses ask and answer different question. Specifically, each ANCOVA draws the following type of conclusion. "Had I …
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It only elongates the x axis (It does not distort text, etc). Initially it looks like everything is stretched, but that fixes itself in a few seconds. https://forum.cogsci.nl/uploads/519/A7E8B609TPI3.png
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For the single plot, you can drag the little triangular handle (that appears below the figure) to the right, to elongate the x axis. (Note: The last time I checked, this action produced a temporary rather than lasting change. That is, if you save an…
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This is just a side comment: If I were reviewing a paper, and if the main effects and interaction results depended on the ordering of the entered terms, I'd be uncomfortable with that. I'd also want to see an analysis (e.g., a regular old rmANOVA) i…
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Actually, in JASP (0.18.3), I haven't found a way to edit/scale-change any of the scatter plots of the raw data, however they're produced (they're all complex, 'customizable' even if they aren't labeled as such). The closest I've come is to produce …
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JASP will give you all of the pairwise comparisons at the same time, if you're willing to accept what many people regard as the correct approach, which is to always use pooled variances.
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Actually, I think there's a legitimate question concerning the unpooling of error-terms. In an ANOVA, there's an assumption of equal error variances. Likewise, for repeated-measures, there's an assumption that the variance of the difference scores i…
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Regarding the after-versus-follow-up comparison in the control condition: Another reason the two ANOVAs produce different post-hoc results is that error term (i.e., variance(s)) for each comparison is estimated from the entire data set--not just fro…
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It's difficult to tell from the screen shots since, in JASP, the repeated-measures cell fields don't always show the entire column name. Maybe you entered the same column name (NI_RT_Forced_...) in two separate fields? Somehow JASP isn't able do fin…
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I think there can also be issues with Type I error inflation if some interactions (or other components) are excluded from the model.
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I think it's quite common for the user to want a 'repeated-measures-ANOVA-like' result, which would include all possible interactions. Thus, even if the default were to exclude interactions, there would still be the question of whether JASP is runni…
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Correction: Chi Sq = Sum_for_Each_Cell [ ((Observed_Count - Expected_Count)^2) / Expected_Count ]
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I'm pretty sure that's the way Chi Square contingency is supposed to work. For each cell, the expected count = (row_total * column_total) / grand total. Thus, the expected count will be zero if either the row total or the column total is zero. Howe…
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@johndavid2 What is a large language model? :-)
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If you run a repeated-measures ANOVA with and without corrections for sphericity violation, you'll see that the corrections do not impact the post-hoc tests. There's nothing more to be done, since the question of sphericity doesn't apply to a repeat…
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I believe you've identified a limitation in JASP that tends not to be present in other stats programs.
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This is what happens when I paste into Word (for Windows): https://forum.cogsci.nl/uploads/574/AFX3FRAU4BZX.png
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Hi. It is not possible to calculate sphericity separately for each of the levels of your four-level repeated-measures ANOVA. Sphericity is a characteristic of the entire model, not of any particular level of the repeated-measures factor. Also, Wel…
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It's not the case that 'effect' of the covariate is controlled in all interactions. It's controlled in all interactions *except*, of course, those interactions that actually include the covariate. 'pse_condition * AQ -> p= 0.044' refers to the in…
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Post hoc tests are t tests that compare one category-level to another (with respect to mean values of the dependent variable). However, a covariate is an interval/ratio variable and so does not produce categories that can be subject to post-hoc t-te…
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I think the spirit of the frequentist significance test is to embrace assumption-violation. The frequentist test of Pearson's r assumes bivariate normality, while the frequentist test of Spearman's rho deliberately violates the normality assumption …
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From a frequentist perspective, there's never any need to have an algorithm that specifically tests a Spearman in contrast to a Pearson's correlation coefficient. To compute and test Spearman's one need only rank-transform each of the two variables …
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OK. Thanks. Here are the conclusions I've drawn with regard to instructing students and others on the use of R to compute new variables in JASP: " The JASP data set is accessed, *not* as an R data frame, but as a collection of *individual vect…
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Thanks. One thing I notice is that with your R code, you are creating a new vector rather than a new data-frame column (it was not obvious to me that JASP only wanted a new vector). However, it's still the case that if I use drag and drop to comput…
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Rather than being unable to complete the non-parametric test, it should generate an error message (it does for me). That's because, while Friedman's test is applicable to a single-factor repeated-measures ANOVA, it's not applicable to any other type…
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Yes, but that ratio appears to always be identical (within the estimation error of the model) to the prior odds of 1:1 multiplied by the Bayes factor, which is simply the Bayes factor (which JASP provides both for the t test output and the ANOVA out…
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Thanks for pointing that out to me. However, below is the JASP output for a Bayesian two-group ANOVA (preceded by a Bayesian t test on the data). I want to know the value of the posterior odds (OP) where: "Given the data, the odds of the altern…
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As an alternative, simplified approach, I would just do a MANOVA on change scores. Each of the five DV's would be transformed to a change score (i.e., post-minus-pre difference score). Thus the design would be a one-factor MANOVA with group (Group …