I launched an app to help people get faster at doing X task. The app has been running for 3 months and I have data for each day on the time it takes to do X task. I have this data for even before app launch, as far back as 3 months prior. So I have 3 months before my app and 3 months after my app.

I want to see if there's evidence that people actually got faster at doing X task after using my app. Any suggestions on what tests I should do?

]]>I enjoyed your Bayes JASP workshop last year very much, thank you again for presenting this interesting approach. Since last year I am trying to use JASP and report Bayes factors in my papers as well.

I conducted a bayesian repeated measures ANOVA, but am insecure how to report it properly.

As a classical ANOVA showed evidence for the Null hypothesis, I calculated the BF01 accordingly (compare to best model). The Null Model seems to be fitting best here (see attached file).

However, I read in an article Keysers et al. (2020) that a BF around 1 means there is no evidence at al.

*"If the Bayes factor calculated as ℒgroup/ℒnullis >1, there is evidence for the effect of group. If BF < 1, i.e., the null model outperforms the more complex group model, there is evidence for the absence of an effect of group. If BF ≈ 1 we have absence of evidence. This Bayes factor can be interpreted using the same bounds discussed in Fig. **2** and Extended Data Fig. **1**."*

As the Bayes Factor B01 is 1.000 in my analysis, is there no interpretation at all possible, neither for H0 nor for H1? I am simply not sure how to report this finding exactly in my paper. This would be my approach:

To ensure that our null hypothesis did not arise by chance, we performed a Bayesian repeated measure in favor of the null hypothesis. We found strong evidence in favor of the null hypothesis, as the null model was the best fitiing model (BF01 = 1.000).

Thank you so much!

]]>Very new to JASP so apologies if I'm missing something really obvious.... When I'm in Descriptive Statistics, I only have the menus for Statistics, Basic Plots, Customizable Plots and Tables. I don't have a "Plots" drop down menu anywhere, although I'm seeing that in the JASP guides and e-books/manuals, YouTube videos, etc.

Is this a part of the newest update and I'm looking at materials created on older versions perhaps? I've uninstalled and re-installed JASP and have the same options.

I've also seen on a previous thread on the Forum that Bar Graphs with Error Bars was planning to be an added option for JASP; has this happened and I can't seem to find it, or yet to be added please?

Thank you 😄

]]>Currently I have to untick and tick back to manually re-run the tests

]]>I am a novice in Bayesian statistics. Recently, I have explored how to conduct Bayesian ANOVA using JASP and interpret results.

I have a confusion that the distinction or relation between Prior and Model Prior in Additional Options. When I modified the input of Prior, the results of BF10 and BFincl changed, and P(M) did not change. Interestingly, when I modified the Model Prior, the P(M) and BFincl changed, but BF10 had no change. Could you help me to solve this issue?

Moreover, I personally understand the calculation of BF10 is posterior odds divided by prior odds,

and BFincl is

So, the change of BFincl is clear to me, but I am confused that BF10 is influenced by the input of Prior and is not by the input of Model Prior. Generally, Bayes factor is independent of prior probability. I have no idea that the modification of Prior affects BF10, whereas the modification of Model Prior changing P(M) does not affect BF10.

By the way, I use the version 0.16.3 of JASP.

I appreciate you answering my questions!

Best wishes,

Wang

]]>Everything still appears to work properly, but I'm not sure if this could potentially cause issues in the future.

Screenshot is attached.

Apologies for what must appear a naïve question.

Regard,

Leigh

]]>I have a question regarding Bayesian mixed effect models. I actually use R to run the models, but I ran into an issue with one of my models and tried to run the same in JASP. In this model, one of my fixed effects has 6 levels. I receive estimates for 5 of the levels compared to the reference level, but first I would like to know whether the fixed effect has a general main effect (i.e., not per level). So far, I could only figure out how to get results for main and interaction effects via ANOVA summary, which is also automatically shown in JASP when I run the frequentist analysis. When I then run the Bayesian analysis, I again only get estimates for the separate levels. Is there any way to get the results in the ANOVA summary table also for Bayesian analyses?

Thanks in advance,

Faya

]]>I conducted a 2x2 mixed ANOVA

(1- within subject with 2 measure, 2- between subject with 2 groups)

I have 2 planned comparisons that i'm trying to find their effect size in JASP but only able to do so in post-hoc.

thank you for your help!

]]>Meir

]]>I am new to JASP and was wondering if there is a way to output the robustness region (minimum and maximum scale factors that lead to the same conclusions, taking, e.g., BF 3 and 1/3 as the cut-off values). The information is presented graphically as part of the robustness check, but it would be very useful to be able to access the interval as well, as it is easier to report this together with the BF than attach the plot for every test.

Thank you!

]]>Thanks

]]>I have to run a bayesian repeated-measures ANOVA with 2 within-subject factors and 3 between-subject factors, so 5 factors in total. While running a ANOVA with 4 factors work works perfectly, running it with 5 factors does not work. The computer (a very powerful one used to process MRI data) runs for several hours and then the page on JAPS becomes entirely blank (see attached file). The JASP version is ont of the latest and we have already reinstalled it. We have also tried on other computers, with different JASP version, same issue.

Is there a maximum in JASP regarding its possibility to compute BF?

Thank you,

Emilie

I am running an analysis on event-related-potential latency measures that were obtained using a jackknifing approach (see Kiesel et al., 2008, Psychophysiology). With a frequentist repeated-measures ANOVA, the analysis of such subsample scores requires the correction of the resulting F-values; that is because jackknifing artificially reduces the error variances in the ANOVA and consequentially, the F-values are too large (see Ulrich & Miller, 2001, Psychophysiology).

However, I now want to run a bayesian rmANOVA - is jackknifed data suitable for a bayesian analysis or does it require some kind of correction as well? I appreciate any thoughts on this.

Thanks!

Laura

]]>(i) Robustness Check:

https://preview.redd.it/fg9c3zocvwe91.png?width=853&format=png&auto=webp&s=e69774c20d8605e0fe4d0c6f2f52a52635df5861 There was an error displaying this embed.(ii) Sequential Analysis:

https://preview.redd.it/tuc8pspdvwe91.png?width=765&format=png&auto=webp&s=a44230accc212e4621acb116f902db3765dea5d6 There was an error displaying this embed.But I don't see the same option when doing a Bayesian ANOVA test.

]]>I have a small question about weighting in KNN classifiers. Just to see if I understood well.

Does "Triangular" weighting that neighbors closest to the "unknown" sample are weighted more in class assigment, and that weight decrease linerly with distance?

Does "Rectangular" (default) means that all neighbors have the same weight?

I have also seen that there are other weighting patters, some with strange names. Do you have some information about them?

Thanks in advance.

]]>Has the test statistic (T-stat) used in Conover´s post-hoc following Friedman´s test been changed in JASP new version?

I was "practicing" Friedman´s test and used Mark Goss-Sampson “Statistical Analysis in JASP - A Students Guide” and his data. However, my values get the same with the exemption of the T-stat value in the Conover test (see attached PDF).

So, question! Has the test statistic for the Conover been modified?

Kind regards

Per

I would like to have access to recently added Quality Control module script in order to add/edit few things. Is it possible?

Thanks

]]>I'm currently using JASP for my master thesis and am trying to figure out how to calculate the size of effect in a Bayesian Repeated Measures ANOVA.

I'd like to report the results like this: BF10 = x, R^2 = y

I read the following in another Paper that used Bayesian Repeated Measures ANOVA: "Effect sizes were computed as the increase in R^2 when adding the factor to the model."

How exactly am I supposed to do this and which values do I need?

Would be thankful for some input/ideas! 🙏

]]>Any news on when JASP 0.17 will be released🤪?

Per Palmgren

]]>I am analyzing data in JASP for a research project and I'm trying to run an independent samples t-test, but I get the error "number of factor levels is not equal to 2". How can I best solve this? Is there something wrong with my data file? In column 1 I have 25 bee species, in the second the number of that bee species in a certain area and in the third the same for another area. How can I best tackle this, I can't figure it out myself. My statistical skills are not experienced, hope someone can help me!

Thanks in advance!

Sincerely,

Joshua Seelen

]]>“Standardized estimates” in (mediation analysis of) JASP, does it do the following**?**:

independently for each variable: for each of its values: subtract value from this variable’s mean and then divide this output by this variable’s standard deviation.

Is this correct?

]]>Hello,

I'm looking for how to include the "standardized" coefficients for second order factors (CFA) in JASP. Only non-standardized coefficients are displayed. See my screenshots.

]]>I have just installed the 0.16.3.0 version of JASP.

What is new respect to 0.16.1.0? At first sight, they looks alike (but I have not tried all tools yet).

Best regards.

Leonardo_C

]]>I know that this question doesn't belong in this forum but the truth is that in the past you have helped me a lot with my OpenSesame related questions, so I hope that this time it will be no exception😊. I am actually writing about the SPSS analysis of my data.

I have run the Shapiro-Wilk normality test and I' ve found some of the variables not being normally distributed, yet my Levene's test for homogeneity was confirming its null hypothesis. All in all, the question is can I still run a repeated measures within factors ANOVA (with a sample of 34 people), even if I don't have normally distributed data but there is homogeneity in them?

Thank you all in advance!

Yours faithfully,

Vasileia

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