patc3
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anyone?
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Wow, I imported a CSV file with 1000 columns it failed (same error), I imported a CSV file with 999 columns and it imported correctly... Why is there a 1,000-column limit, is this by design or is this a bug? Does it do this for any types of imports …
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See my response here (with gifs): https://forum.cogsci.nl/discussion/comment/27485#Comment_27485
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The method is the so-called "product of coefficients" approach: https://psycnet.apa.org/fulltext/2002-00925-005.pdf MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test me…
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PLS SEM isn't the same thing and doesn't use lavaan (it's got different syntax). If you want to use PLS SEM (which I'm not sure why you would) then look up syntax for cSEM library in R, that's the library that JASP uses.
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Yes you could copy paste lavaan text from mediation and put that into SEM.
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It's not using Baron & Kenny. I don't know what the Hayes approach is--what the approach used is is what I think to be the MacKinnon approach, i.e. you multiply the a and b paths and test for significance (the product approach). That's the stand…
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ouuuuuh I didn't even realize there was a difference between medians and medoids! interesting
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In the meantime, you can create as many intermediate variables as you need to reach the final result, e.g.: var2 = ifElse(var1 = 0, 0, 1) var3 = ifElse(var2 = 1, 1, 2)
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You can perform rotation using the UI directly in JASP (e.g. in the principal components analysis or the exploratory factor analysis). There's dropdown menus there to select which kind of rotation. edit: sorry I just saw you're trying to get $rms fr…
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Seeing as you're using Gaussian with identity link, have you tried using lineard mixed instead of generalized linear mixed? (Linear mixed is Gaussian with identity link)
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What I meant was I'm pretty sure it actually is equivalent to an ANOVA, not just "like" an ANOVA. That's how an ANOVA is done in the Bayesian framework. If you compare results, pretty sure they'll be basically identical (within Monte Carlo…
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Hmm, I don't know any other way than through a hierarchical model to estimate a RM-ANOVA in the Bayesian framework (that's how I learned it anyhow, through the Kruschke book). Hierarchical models are much more natural (and prevalent) in the Bayesian…
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There is a bona fide RM-ANOVA under the ANOVA menu, select repeated-measures ANOVA (with post-hoc comparisons and effect sizes and the whole shebang): https://i.imgur.com/TugifUG.gif https://i.imgur.com/TugifUG.gif
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Sorry not off the top of my head... you can go on Google Scholar and search for "multilevel logistic regression" or something like that, for example: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935148/
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Yes, in mixed models > generalized, with binomial family and logit link function
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No need for model <- "...", only the model in the dialog box. No quotation mark, no assignment to a variable called model. Only the model syntax
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Pretty much the same syntax, except that formative constructs (i.e. composite variables) are specified using the <~ operator (e.g. "f1 <~ item1+item2+item3+item4) You can probably get more info by clicking on the "i" information…
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The simplest in your case is to download a previous version of JASP: https://jasp-stats.org/previous-versions/ Note that it's very hard to get an idea what the problem is if you don't give any information about the problem/error received/screenshot/…
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You might have one row (or several) that have something other than a number? or if some of them are blank, maybe it's a space?
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See my comment on another thread here: https://forum.cogsci.nl/discussion/comment/27658#Comment_27658 The answer to your question is probably no, it's difficult to compare continuous and categorical predictors together
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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 …