František
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Hi Herculano, Thanks for the nice comments! Regarding your question, there are two things at play: 1) The R-output seems to do a subgroup analysis on the data, i.e., fitting an independent random effect meta-analyses to the same data set. Consequent…
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Hi, There are many possible approaches to this - one of them is indeed a network analysis (which is not supported in JASP yet) but you could also do subgroup/meta-regression analyses for each domain/condition and include random effects for additiona…
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Hi happydad024, We rolled out one part of the update in November: the options and output are much enhanced, but these pair-wise comparisons are still missing. Luckily, they were just recently added to the metafor package, and I'm planning to include…
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Dear Yuichiro, Do I understand correctly that you are having issue specifying the dependent variable into the first input field (i.e., where you black square box is)? There does not seem to be any additional variables left that could be used as the …
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Hi Yuichiro, I'm sorry but I couldn't follow your explanations. Could you please upload a screenshot of the settings and what you are trying to do? Best, Frantisek
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Dear Wempy, I'm sorry but we don't have diagnostic features for GLMMs in JASP yet. We are however planning on adding them in the future. Cheers, Frantisek
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Hi, you have two options: you can use a meta-regression where you use the variable as a factor predictor. This allows you to estimate the treatment levels at the different levels of subgroups. You can use the "Estimated marginal means" to …
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Hi Emily, 1) I think that's quite an acceptable approach. (There are different suggestions for how to go about this, but there is no clear guidance. Other are also not accessible from JASP, e.g., covariance matrix eigen decomposition etc.) 2) There…
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Hi Emily, There are two reasons in an interplay here: 1) the main effect is just significant 'p = 0.02'. With more than two factor levels tested via an F-test, the F-test might be significantly while none of the pair-wise comparison is. (The reason…
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Hi Robbin, Sorry for not responding to this for so long, I lost the email in my mailbox. The good news is that since your question we released a new version of JASP that allows to fit random intercept models, so you should be able to get the estimat…
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Hi Flejeu, If you click open the "Model" section, there will appear the "Fixed effects" box with your two already specified variables. To add the interaction, you need to shift-click selected multiple variables from the "Mod…
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There is a lot of discussion in the literature how to properly specify random effects structure. However, one thing that majority of people agree on is that random intercept only models are suboptimal as they lead to poor coverage and inflated type …
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Hi barrychow, What you see in the analysis is a meta-regression with the "culture" corresponding to a dummy-coded predictor. The intercept corresponds to the estimate in the level "1", and the coefficients "2" and "…
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I'm sorry but I don't have any suggestions in this regard.
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Hi Naomi, Currently, you can't see that, unfortunately. You can inspect the random effects variance (which would be an indicator of its contribution) under the "Variance/correlation estimates". If the random effect's variance is essentiall…
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Hi, Unfortunately, the JASP does not contain assumption checks yet. I was planning to expand the module after the current release, so this is definitely something we will be looking into. Cheers, Frantisek
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Hi Philip, thanks for reporting the bug, we will fix it with the next release! Cheers, Frantisek
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Hi Katie, The resulting effect size measure is solely determined by the input. In other words, if you use Cohen's d as the input, the output is going to be on Cohen's d again. If you use correlations as the input, the output is a correlation (I woul…
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Hi Howard, Yes, I can see that as a useful feature indeed (so far, you can obtain the OR by exponentiating the non-response scale output manually--in case someone else comes to this thread later). Thanks for looking into this with me, Frantisek
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Hi Howard, Thanks for catching that out--I haven't used emmeans for quite a bit and didn't know about this. I finally had a bit more time to take a deeper look. Using the following code, it seems like that using regrid (as we do in JASP) helps with …
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Hi Andrew, You can get OR from the contrasts only if you don't use the "Outcome scale" option. Otherwise, the contrast is a difference in the probabilities of those two outcomes. Glad I could help, Frantisek
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Hi Howards, Hmm, that's quite interesting that R produces the output on OR scale (I would expect it to use the specified contrasts by emmeans directly). Also, in JASP we use a combination of the two functions too, i.e., https://github.com/jasp-stats…
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Just to check, did you use the 'emmeans::emmeans' function to create the marginal means object and then the 'emmeans::contrast' function to compute the contrasts? We did explicitly separate the calculations in the underlying implementation to always…
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Dear Andrew, I'm slightly confused about your question now. When you have a factor with two levels, the sum contrast creates (-1, 1) coding for the levels (i.e., contr.sum(2)). In other words, the fixed effect estimate corresponds to half the differ…
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Hi Howard, Thanks for posting the question. I think that the main difference is that you do not compute the contrast on the outcome scale (probabilities) but rather on the latent model scale and then transform it into odds ratios. I.e., under the sp…
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Hi Andrew, The contrast coding can be indeed a bit confusing. We added a more detailed explanation to the help file that summarizes what are the differences and reasons for using the sum contrasts (where the fixed effects are not readily interpretab…
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Hi Jonathan, The prior distributions are weakly informative and should be well-behaved in parameter estimation settings. The module uses the default prior distribution settings of the rstanarm R package which defines normal(location = 0, scale = 2.5…
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This seems to be an issue with the maximum length of R formula -- it cannot be longer than 500 characters. In such a case, I would recommend shortening variable names (e.g., Emotion -> Em, Stimulus orientation -> StOr, etc.). In case that does…
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We resolved this via email with the help of Thomas Debray. I'm attaching the response if other people have a similar question: The I2 statistics you report are OK, they usually represent the I2 of the logit transformed c-statistic (unless no transf…
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Hi Peter, Yes, EJ is indeed right. If you input the Bayesian posterior estimates, you are adding additional information to each study via each t-test's prior distribution. The `frequentist t-test's` Cohen's d is just a summary statistic used as an …