patc3
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I don't think it'll matter in that case. Not sure it would ever matter in JASP (I don't know), but for correlation and regression the variables shouldn't be treated differently whether they are scale or ordinal.
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What analyses do you want to do with these variables? Also, how many distinct values do they have?
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Again, this could still be due to the interaction terms. Have you tried including age without interaction terms? My guess is still that the problem is due to interaction terms with latent variables. I have personally never dealt with this problem, b…
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I don't know for sure what the source of your problem is, but just bouncing ideas here: I don't think interaction terms with latent variables are implemented in lavaan (the SEM library JASP uses) yet
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I've now discovered that the default model in the mixed-models module is way too complex: by default, all possible interaction terms are included as fixed effects (under the Model section in the analysis), and all possible random slopes (including i…
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the reason is they're not really meaningful with categorical predictors. A standardized coefficient is the effect of x on y when both x and y have been standardized, which means they each have had the mean subtracted from their values, and divided b…
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Hi Mikel, it's really tough to see where the problem is without seeing what the data look like... What about Congruency, are you sure it's only got 2 different values? I'm bothered by the fact that it's a text variable Some descriptive statistics mi…
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I think it's because those are so-called adjusted means, i.e. means adjusted for the effects of the other factors included in your ANOVA. What happens if you test with a simple one-way ANOVA (only your factor)? In that case the means probably match …
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With repeated measures ANOVA you get listwise deletion, i.e. any observation with a missing data anywhere on your 3 assessments will be removed from the analysis. This is not specific to JASP. You should look into mixed effects models instead. Take …
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Wow, thanks for the tip, you're right it's well hidden--I told a student recently that JASP didn't seem to do multinomial logistic regression just yet!
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1) changing the reference category: In the data view, click on the variable name, the reference category will be the first value at the top. To change the order of the values, you can select a row and use the up and down arrow. So you can put the ca…
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Under "Options", test method (on the left) is set to Satterthwaite by default, you can change it to Likelihood ratio tests, like so: https://forum.cogsci.nl/uploads/747/23YCGD1MPDFP.png The output now shows ML estimation: https://forum.cog…
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Yesterday I discovered that the current version has a new bug with the correlation analysis where you can't check/uncheck correlation types or else you get a table full of NaNs... The warning (which says it'll be fixed in 0.17.2) says to refresh the…
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The JASP manual has decision trees at the end to help you choose which test to use: https://jasp-stats.org/wp-content/uploads/2022/04/Statistical-Analysis-in-JASP-A-Students-Guide-v16.pdf More generally you should take a look at the JASP website, th…
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one of your factors has fewer than 2 observations in one of its levels
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In the meantime it might be possible for you to manually create time variables (e.g. time1 = 0 1 2 2 2 and time2 = 0 0 0 1 2 3 has two splines/one turning point) There's a section on piecewise models in Coulombe & Selig (2015), as well as in the…
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this is just to choose the number of components/factors, not how the actual analysis is conducted. So while I'm not an expert in this I suppose if you're doing PCA it makes more sense to choose the number of components with the parallel analysis bas…
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The test is descriptives is univariate (i.e., does each variable follow a normal distribution indiviodually?), while the test in the correlation analysis is either bivariate (do both of my variables taken together follow a bivariate normal distribut…
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Thanks EJ, #1 and #3 are great news
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using R code you can do: rowMeans(cbind(var1, var2, var3), na.rm=TRUE)
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Factor A: F= -1.02e-12 (on R) vs. F=2.878 x 10^-5 (JASP) Hmm negative F-test isn't even possible...
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Hi JP21, I suppose you've done this already but while waiting for the user guide, you can take a look at the documentation for the linear mixed models analysis in JASP by click the "i" button in the analysis
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Can you show an example of SPSS syntax of the analysis you want to replicate? (if you're going through the dialog boxes in SPSS, click "Paste")
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to specify which paths are equal across groups, you need to set the labels to be the same across groups. For example, if you have two groups and want to fix the factor loading of cm1 to be equal across groups, your last line would be: "cm =~ c(…
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It looks like JASP v0.17 uses FIML by default for both CFA and SEM, and you can change this option. If you prefer to use the SEM analysis in JASP instead of the CFA analysis you can specify the syntax for the measurement model manually (e.g. f1 =~ i…
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This might not be ideal but as a temporary fix in the machine learning module you can uncheck "Scale variables" under "Training Parameters" (both for decision tree and SVM) For the regression module you get the coefficient of det…
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Btw I tried some stuff but couldn't figure it out either... am curious to see as well
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Seems to say you have fewer than 2 people in some group(s) in "prlang"
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Edit: Sorry I spoke too fast, I tried with a factor predictor with more than 2 levels and I'm no longer reproducing the fixed effects, seeing as I would need to change the coding as you mention How did you get the chi-square in R? when I do the foll…
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Hello, I'm also curious about this (seeing as the results are *very* different). Could I also check out the data or jasp file? My email is patrick.coulombe@d22consulting.com By the way R-hat of 1.145 seems to indicate the chains have not converged (…