I ran the Mann-Whitney independent samples t-test using the frequentist and Bayesian approach. I obtained a p value of 0.003 whereas the Bayesian factor was just 1.003 (with Group 1 ≠ Group 2). What could explain this discrepancy between the results? I attached the tables. Thanks!

I'm learning to perform a Network Analysis in JASP. I am wondering if JASP can calculate the bridge expected influence, the correlation stability coefficient of bridge expected influence and bootstrapped difference tests for bridge expected influence. (I have divided my nodes into two groups/communities)? If so, what are the steps for these procedures？

Thanks :)

]]>I really Need your help. I cant figure out, how To do an mediansplit in jasp. I Have a likert scale from 1-7. the median is 5. i Need 2 groups. 1 group with a median below 5 and a group with an median above 5. how am i supposed To do this mediansplit? Thank you for your help!

]]>I'm trying to run the generalTestBF() function on a linear mixed model.

I get the following error : Error in logExpXminusExpY(logPriorProbs[1], logPriorProbs[2]) : function 'Rcpp_precious_remove' not provided by package 'Rcpp'

I have tried install.packages("Rcpp") and update.packages("Rcpp") followed by library(Rcpp), but I still get the same error message.

Do you have any pointers as to what I should try next?

Thank you very much,

Emma

]]>Thank you,

Anoop

]]>I am trying to understand the results of a Bayesian ANOVA.

I am assessing the influence of two periods of competitions (congested and non-congested) on soccer players physical performance (e.g., distance covered, accelerations, sprints, etc.).

I considered physical performance variables as a "dependent variable", the competition period as a "fixed factor", and the players were considered a "random" factor.

Similar to previous studies where I performed a Bayesian ANOVA, the **model comparison table **present the null model in the first line, with a BF10 equal to 1.000 (please see picture below).

However, when I consider the **distance covered** variable, the model comparison table is quite different, showing the **fixed factor in the first line **with a BF10 equal to 1.000 (please see picture below).

Could you please explain to me why this happen? Do my data (table 2) provide no evidence that the competition period influence the players' distance covered?

Thanks in advance,

Mateus

]]>I have an issue with running JASP 0.15.0.0 on my MacBook Air (2017, Processor 1.8 GHz Core i5, Memory 8GB) running on BigSur Version11.2.3. I downloaded the dmg file successfully yet when I am about to start it, it shows the message below:

Is there a workaround to this? I am worried that it has something to do with the OS itself refusing (and its future versions) to cooperate with JASP.

Many thanks in advance! Stay safe everyone!

]]>I am rather new to statistics. A question! When running a RMANOVA in JASP what does the "Between Subjects Effects" tell me (see below)? I this a default setting? Why is there no F-statistics of F-value. Happy if somebody could help me to better understand :-)

Per

When running a RMANOVA it seems like pool error term for RM factors is pre-checked. Could somebody enlighten me in which circumstances to check it and when NOT to have it checked? 🙄

Thank you for guidance:-)

Per

I have two treatments (A and B). All participants completed both treatments on separate days (within-subjects design). Participants completed the same cognitive test under both treatments once at the start (pre) and at the end (post).

My advisor has now suggested calculating an ANCOVA since the pre-values differ between the two treatments. To do this, I tried to do the following:

- Open Repeated Measures ANOVA
- Add both treatments as levels of RM factor 1
- Add both post-measurements as outcome variables (repeated measures cells)
- Add both pre-measurements as covariates

Is this correct? I am not sure if this is correct, since here, the pre-measurement of treatment A would also serve as a covariate for the post-measurement of treatment B (and the other way around). This sounds like a problem to me, but I am not sure...

So basically, my question is: Is it possible to do an ANCOVA in a pre-post within-subjects design with the pre-measurement as a covariate?

Thank you in advance!

]]>This is my first time working with JASP as part of a seminar paper and I don't have much prior statistical knowledge. So far I have done a confirmatory factor analysis and a regression using SEM (lavaan). As a measure of validity we are supposed to report the Composite Reliability and the Average Variance Extracted, among others. Unfortunately, I have not found a function for this in JASP yet. When I looked at the Factor Covariances table, I noticed that only the covariances of the independent variables were measured and the dependent variable is not listed. How can I change this? Ideally, I would like to have a correlation table that also maps the Composite Reliability and the Average Variance Extracted.

Thanks a lot in advance!

Best regards

Carolin

]]>it appears that JASP 0.15 no longer displays post hoc t-test effect sizes in RM ANOVAs. My limited testing suggests that this only happens for interaction effects. Please let me know if you need more information.

]]>I find the new version of JASP is slower and frequenctly goes to "Not Responding".

Please note, I am using the same data I used in the previous version and did not have this issue before.

Appreciate your advise on this.

Thank you,

Sameha

]]>I wanted to ask which underlying R function JASP uses to run the parallel analysis in order to quantify the number of factors for an exploratory factor analysis?

I ran the same EFA in JASP and R using the fa.parallel function (psych package). With the fa.parallel function, I get 6 factors no matter which factor method I use (minres, ml, wls, gls, pa). However, JASP outputs 10 factors when I use the parallel analysis option to extract the number of factors.

Looking at the scree plot, I can see that one could argue for two different points of inflexion, yielding either 6 or 10 factors. However, based on the scree plot, I would think that 6 factors are the more straightforward solution and I am puzzled that the parallel analysis in JASP yields a different result. Therefore, I would really appreciate your help!

]]>I am adding this as a new discussion, as there were not any responses to the older discussion on this that I followed up upon (https://forum.cogsci.nl/discussion/5382/parallel-analysis-for-efa-in-jasp#latest)

I have the same issue as described in the discussion above: JASP always suggests a different number of factors compared to R in parallel analysis, irrespective of which fa.parallel() settings I use, i.e., SMC = TRUE does not make a difference. I have tried this for 3 different combinations of questionnaires (different combinations of items from the same data set in this case), and I have repeated the parallel analysis several times.

Could there maybe be a different explanation for why this happens, other than that the difference is due to chance (because the simulated data can be a little different every time) as pointed out by @evankesteren? The factor loadings are identical as expected when I use "minres" for both. And it seems odd to me that there is a consistent considerable difference due to chance.

As an example, R suggests 3 whereas JASP suggests 1 factor here. I can see how both make sense, but this happens for any combination of items.

https://forum.cogsci.nl/uploads/477/QA52G3T7T33I.png There was an error displaying this embed.Any help is appreciated!

]]>I am looking for help how to set up JASP in the following case:

a cress test has been run with 4 different substrates and 4 different levels of a material called FerroSorp. In every Petri dish 10 seeds where placed. After the experiment the root length of each seed in all the different variants was measured. Every variant has three reps (=Petri dishes). Now I would like to run an ANOVA that compares the data without starting with the average of each Petri dish but instead using the information of all the 10 seeds in each dish. How do I have to set up this? Please find the csv data attached. I guess it's a repeated measure ANOVA but am not sure how to set the inputs right :/ Many thanks for Your help.

Friedrich

I search for plot to graph two nominal variables using mosaic plots but I don't find this plot method. Is it somewhere hidden ?

Thanks

ftr

]]>Let me start by apologising for the naive question, but I want to make sure I understand the output of my JASP analysis (and to resolve some disagreement between colleagues). So, we performed a Mann-Whitney Bayesian Independent Samples test. The alt. hypothesis was set to show that the Group 1 and Group 2 are NOT equal (Null obviously, that they are). If we are reporting BF01, and our results show that BF01 = 1.2 is it correct to write: 'The Bayesian analysis yielded BF01 = 1.2, indicating that present sample data are approximately 1.2 times more likely to have occurred under null (no difference) than alternative hypothesis (difference), thus we also do not find clear support for the null.

Again, sorry for simple question and thank you for any help.

/Martyna

]]>What kind of data do I need to collect?

number of patients in the study

events in the study

number of patients in the control

events in the control

Is there any particular order in which I arrange the results?

Are there any documents helping to answer these questions?

Regards

Peedikayil

]]>I was invited to post my request about the network analysis from the JASP facebook page.

I have a database made by ordered + continuous variables, thus I used the "correlation" as estimator and the "cor_auto" as option. By reading JASP guide, in this case the weights matrix is computed according to the polychoric or tetrachoryc correlation rather than Pearson correlation.

Therefore, I would know if it is possible to display the p-values in the weights matrix arising from the network analysis, since JASP does not enabel to compute polychoric and tetrachoryc correlations with another module.

Thank you very much in advance for your help and for this great software.

Giovanni

]]>how is the Bayesian d-value (delta) distribution calculated in the independent-groups t-test? In particular, I'm curious about what SDs is used (assumes a single SD underlying both groups? pools group SDs?) - but a general description for estimating the distribution of delta

...is there a technical manual for JASP? I'm happy to dig into that instead - thank you! Fred

]]>(1) There's a recurring announcement that "JASP has been installed from a Zip. . ." However, it is not a one-time thing. It happens every time I double-click a jas file to launch JASP. See the image, below.

(2) There's a 3- to 5-secod delay (with no indication that JASP is working on it) in the engagement/disengagement of variable-level filtering. See the image, below.

A clue on how to search this forum without getting every post with the work ifElse in it would be helpful as well.

Thanks much.

I'm using Bayesian statistics for the first time on Independent Samples T-Tests and Kendall's Rank Correlations on JASP.

After I presented the results to my colleagues, one of them responded with the following comment : ''Before presenting any Bayesian analysis results, you need to present convergence diagnostics. In the methods you need to state how many MCMC simulations you ran, and in the results you need to show that the models converged.''

A recent paper (Kruschke, 2021) presented reporting guidelines and mentionned the importance of this information. They suggested reporting the Potential Scale Reduction Factor (PSRF or R-hat) and the ESS (number of steps).

I have looked everywhere I could think of for these values but have not found them in JASP options and outputs.

Thank you for your guidance,

Emma

]]>so I am running the same Data through a GLMM in JASP, MATLAB & R to get an overview/confidence in my results. Its a simple Generalized Linear Mixed effect model on reaction times being influenced by the stimulus type and the movement type that was to be performed.

While MATLAB and R have congruent results JASP shows some oddities. As to be seen in the attached images. Especially odd is the difference in DF, where MATLAB consistently choose the number of trials (7063) and JASP chooses the Number of participants (~23) for the intercept. This is very odd and does not react to any changes in the model.

If you have any idea why this is the case, please let me know!

How do I transpose variable rows to columns, OR change data types with variables in rows?

Thanks, Branden

]]>