It's my first time interpreting a bayesian one-tailed two-sample T-test. If I understood correctly this outcome means that BF (0+)=4.93 indicates moderate evidence in support of H0. The data is 4.93 times more likely under H0 then under H1.

The alternative hypothesis states that people in group 1 show more conformity than people in group 3.

If the alternative hypothesis specifies that location of group 1 is greater than location of group 3, does it, in return, mean that there's moderate evidence that group 1 doesn't show more conformity than group 3? or does it mean that it's 4.93 times more like for group 1 not to show more conformity than group 3?

I don't know how to apply the data to the actual hypothesis.

I also did the bayes Factor robustness check and I was wondering if this means that it is not robust due to the span. Is this correct and is there anything else I should extract from the robustness check?

What would be the next step knowing that it's not robust?

Thanks for your help!!!!

I have a question how can I deal with dependency in meta-analysis in JASP? I have a few outcomes (from one study) and want them to be included in the analysis. How can I avoid a dependency problem?

Only to mention that RevMan - one of the most popular meta-analysis software, does not have it. I know there are R=packages (e.g., robust variance estimation, RVE) but what about JASP?

The problem, as I read, is ubiquitous.

Perhaps see here:

https://www.ncbi.nlm.nih.gov/corehtml/pmc/pmcgifs/pmc-card-share.jpg?_=0 There was an error displaying this embed.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4191743/

thank you

Stan

]]>I have two questions regarding the Data Library "Larks and Owls"

Q1. When I checked the "Fixed effects estimates", a table including Chronotype (1), Chronotype (2), and TimeOfDay (1) appered. Is there any way to know what exactly these values mean? (In a future version I hope to see it as ChronotypeMorning instead of Chronotype (1).)

Q2. I'm not sure if I correctly understand how to specify contrasts, I think there is a problem with contrast 6. In contrast 6, there are three "1"s and only one "-1". Is it correct? In addition, as for contrast 7, I do not understand why such an assignment would imply that the difference of the effect of task completion time between the *Evening *and *Morning *chronotype participants.

Best regards,

Daiichiro Kuroki

]]>One question!

in JASP when running a regression only standardised beta coefficients are displayed for scale variables.You can of course force categorical variables to scale variables. So, in a multiple regression with both continuous and categorical variables is it possible to interpret the standardised beta coefficients? Thus, can the standardised beta be used to understand which variables (be it continuous or categorical) contribute most to the model?

Grateful for a reply.

All the best Per “the JASP lover”

]]>Per

]]>I have implemented EFA on a dataset with two samples, and now I want to implement Multigroup CFA to confirm the factors produced by EFA.

May I ask if there is a document explains EFA, CFA, and Multigroup CFA in JASP? How to to use the functions and interpret the results, and what the recommended thresholds are for interpreting the results?

Thank you!

]]>One question! Is it possible (or will it be in the future) to run a possion regression in JASP?

Per “the JASP lover”π

]]>In JASP, to perform Principle Component Analysis, there are three options: Parallel analysis, Eigenvalue, or manual.

For the Parallel analysis option: it can be either "based on principle components or based on factors with seed 1234". what are these tests?

Is Parallel analysis based on Factors still PCA test? or is it a factor analysis?

]]>One question! Why are the collinearity statistics (VIF and Tolerance) in the regression module the same (see attached file)? How are these calculated? These statistics are not the same when I try to run the same data in other programs like SPSS, DATA and Jamovi.

All the best

Per (the JASP lover)π€©

]]>How can I calculate correlation eta in JASP?

I have nominal and continuous variables. I need to know the correlation eta and significance level.

Any thoughts?

Thank you!

stan

]]>I can’t find this feature in jasp. In SPSS it is:

“**Nominal by Interval.** When one variable is categorical and the other is quantitative, select **Eta**. The categorical variable must be coded numerically.

- Eta. A measure of association that ranges from 0 to 1, with 0 indicating no association between the row and column variables and values close to 1 indicating a high degree of association. Eta is appropriate for a dependent variable measured on an interval scale (for example, income) and an independent variable with a limited number of categories (for example, gender). Two eta values are computed: one treats the row variable as the interval variable, and the other treats the column variable as the interval variable.“

in SPSS it can be find here

Specifying Statistics for Crosstabs

This feature requires Statistics Base Edition.

- From the menus choose:
**Analyze**>**Descriptive Statistics**>**Crosstabs...**- In the Crosstabs dialog box, click
**Statistics**.

Is it possible to calculate it in JASP?

]]>]]>

I am currently conducting six regression analyses with 4 IV and 6 different DV. Furthermore, I include 4 covariates in the models. Following the paper of van Doorn et al. (The JASP guidelines for conducting and reporting a Bayesian analysis), I want to assess the robustness of the results. I used the default prior option, because prior knowledge is absent in my case (so: r scale = 0.354). I would like to check manually if my results are robust (by setting different priors). My first question is: how do I choose my other priors? Does it make sense to use r scale = 0.1, 0.5 and 0.7? Or are there "classical" / recommended priors I should use to compare my results with the default priors?

My second question relates to the interpretation of the robustness check. I saw that this analysis: https://osf.io/wae57 (mixed ANOVA without covariates). The authors stated: "Since we do not have random effects or covariates in the model, only the hyperparameter for fixed effects is relevant." Now I am wondering how to interpret the results of my regression models because I included covariates and conducted a regression. Which indices do I use in order to compare my results for different priors?

I hope that my questions are clear and that somebody can help me out. I was looking in the literature, but maybe I did overlook some information.

Thank you very much!!

]]>I use Jasp for my first study but i’m little lost…

I’m doing 3 test’s on 2 person who have a disease, I treating them and see the amelioration one week after.

One test is quantitative and the 2 Others are qualitatives

How i put my result on excel and what is the test i have to choose ?

Thank you !

]]>I have been getting different results in JASP and SPSS for Box's M test.

For example:

In SPSS, X2 = 28.033, p = .390

- df1 = 21 AND df2 = 3670.442

In JASP, X2 = 22.339, p = .380

- df = 21

In this case, other MANOVA results were the same in both SPSS and JASP. However, I have also experienced times when the results of the f-tests were also different.

Has anyone else ever had these problems?

]]>I`m doing an analysis of a survey for a uni project and tried using JASP. I want to do some plots of the answers for a first review using botplots. Doing them for individual questions works fine but i would like to have several boxplots next to each other since they use the same scale (a 1-7 scale [1 agree completely to 7 dont agree at all]). Im thinking of doing this via the split option but cant get it to work properly.

So i have several columns with data, each a different question and the answers as data in the sheet as numbers 1 through 7. How can i plot a boxplot with the 1-7 scale on the y-axis and split by the question (e.g. q1-q5) on the x-axis with an individual boxplot for each question. Do I need to add additional data to my table so i can use this new data (a new column with some data depending on the questions i want it to be split by) as a data set to split the others? Is there a better way?

Sorry, english isnt my first language and its a bit hard to explain what i want to do. Do you understand the problem i have?

Thank you in advance!π

]]>On the one hand, I can see this as joint multinomial because n is fixed (200), and the assignment is random--I did not select subjects to be in either group based on any criteria. Per Jamil et al., "...this scheme holds when the stopping rule is 'collect data from 100 cars and then stop.'

On the other hand, it seems like a standard experimental design that people have been advised to use the independent multinomial because the comparing the groups which are fixed (but randomly assigned). Jamil et al., state, "...assume sampling based on independent multinomial scheme, such that the crucial test involves a comparison of two proportions." And in the example in the paper, they say subjects were picked based on sex (males = 50 and females = 50); then again the Dutton and Aron example at the beginning of the paper is (presumably) includes a random assignment.

The DV is in the rows and the IV is in the columns.

I suppose it is the *random assignment *that is throwing me off. Any advice would help.

In the meantime I'm trying to use it, and am using the Bayesian Linear Regression example (World Happiness dataset) as a guide. However, I'm getting different results in version 17.2 than were produced in 17.1.

I had installed 17.1 in preparation for a workshop on May 11. The presenter walked us through the Bayesian Linear Regression example. I discovered that 17.2 was released that very day, with Bayesian logistic regression, so I installed it as soon as I could. Now when I run the Bayesian Linear Regression example in 17.2, the output in the model and coefficient tables is not the same, e.g. BFM values are different (I can tell by comparing to the recording of the workshop and to the annotation in the example itself).

Is anyone else having a similar problem with that example? One reason why it worries me is that I'm trying to run a Bayesian logistic regression and the output is odd in the same way as the example linear regression output, i.e., the BFM and BF01 values don't corroborate the identification of the best model.

This all may be due to me being an extreme novice, but I'm concerned that there may be a bug, so I would appreciate anyone who can shed some light.

Thank you!

]]>The "mode" should be translated as modalna or dominanta, not tryb

Is there a way I could pass it to the translation team, or correct the translation myself?

Regards,

Grzegorz

]]>when calculating an RM ANOVA, I encountered a problem. Under "Descriptive Plots" you can set that confidence intervals are displayed. However, these confidence intervals do not match the confidence intervals that are output by SPSS. The confidence intervals in JASP are much smaller. I have attached screenshots of the JASP graph and SPSS graph. Both analyses are based on exactly the same data set. CI's were set to 95% in both cases.

What could be the reason for this?

I am trying to run a Bayesian ANCOVA in JASP with one fixed factor with three levels and one (continuous) covariate (added to the null model).

I have noticed that when conducting pairwise post-hoc tests, the inclusion of the covariate does not seem to have any effect on the results (i.e. Bayes Factors etc. for the post-hoc comparisons are the same whether the covariate is included in the model or not).

Is there any way of conducting post-hoc tests that take into account covariates as it is possible in the case of the frequentist ANCOVA?

Many thanks for your help!

Best wishes,

Julia

]]>when performing a Bayesian RM ANOVA (JASP 0.17.1, macOS, Intel) I don't get any credible intervals (CI) for the descriptive statistics despite having selected "Descriptives" and being able to change the probability of interest of the CI (which has no effect). Below is a screen shot of the results that I get for the descriptives.

According to the guide "Bayesian Inference in JASP: A guide for students" (May, 2020, http://static.jasp-stats.org/Manuals/Bayesian_Guide_v0_12_2_1.pdf) , page 92, JASP should show CIs for the descriptives.

Any help greatly appreciated.

Best

Carsten

I have another question with regard to Bayesina linear regression analysis (I would like to conduct six regression analyses (6 DVs) with 4 IVs and 5 covariates.

1) Linearity: As recommended in the turorial on Bayesian regression of van den Bergh et al. (A tutorial on bayesian multi-model linear regression with BAS and JASP), assumptions of linearity should be checked first. I have attached my correlation plots (page 1). As my relations between the four IVs and the six DVs didn't look linear by eye, I log-transformed the IVs. However, I am not sure if the assumption of linearity is no longer violated because the plots do look a little bit strange (page 2).

2) Non-normally distributed residuals: Are there any indices indicating when the residuals are not normally distributed, by inspecting the plots residuals vs. fitted? Because I am not sure if my residuals are approx. normally distributed (I attached the plots, page 3/4), especially DV 2 and DV 3. Furthermore, I have no idea (based on theoretical knowledge) which term I should add to the analysis (e.g., interaction term).

And conerning both assumptions: I learned a long time ago that violations of linearity and non-normally distributed residuals can be "ignored" with larger samples sizes. My sample size is approx. 180. Still, I am wondering if I can ignore these assumptions?

Thank you so much for your help!

Alexa

----

As I was not able to upload my attachment (neither in word nor pdf): "Request failed with status code 413", I inserted the screenshots. I hope it works like this.

]]>I am a beginner in linear mixed models and would like some advice on what I would like to do with my data. I work in the field of cognitive neuroscience and my research focuses on understanding face processing in adults. We measure face processing using eye-tracking measures (here pupil dilation) and a paradigm using social (images and videos of real faces and avatars) and non-social (objects) stimuli.

I would explore if the physiological engagement, indexed by pupil diameter variations, is caused by the motion. To do that, we quantified the motion amount for each video by a coefficient. My data set consists of 7 variables (participant, movements, actors/actresses, categories, movement coefficient, and pupil dilation) with 1320 observations.

- categories are broken down into 3 components: object (non-social stimulus), avatars, and real faces (social stimulus)
- movements are broken down into 3 components: static, micro, and macro movement
- actors/actresses: there are 4 videos (2 actors+2actresses) per category per movement
- movement coefficient: one coefficient for each stimulus, as it depends on the actors and actresses. They are different people so the quantity of movement is not identical according to the movements (micro movement -> neutral expression; macro-movement -> neutral to happy and neutral to sad). When the movement category is static, the motion coefficient is 0 (only for avatars and real faces as it is static photography but for objects, they are videos with really small motion coefficients and smaller than micro and macro movement)

I thought a LMM would be an excellent analysis to answer my question, as I can consider the fixed effect and random effect but I am a bit lost about the model's writing... I tried random effect for a particular participant to allow the deviations of the intercept of that participant's pupil dilation from the population. In addition, I was thinking to add another random effect for a particular motion coefficient where the deviations in the ordinate of the pupil dilation of the motion coef in question from the total motion coefficient sample.

I have tried several LMMs but I don't know if the models I have tested are correct in writing about my research question: can motion predict pupil dilation? is the motion quantity influence pupil dilation?

modela <- lmer(pupil_dilation ~ categories*movements*actors + (1 | participants) + (1 | motion_coef), data = data, REML = FALSE) modelb <- lmer(pupil_dilation ~ categories+movements+actors + categories:movements:actors + (1 | participants) + (1 | motion_coef), data = data, REML = FALSE) modelc <- lmer(pupil_dilation ~ categories+movements+actors + (1 | participants) + (1 | motion_coef), data = data, REML = FALSE) modeld <- lmer(pupil_dilation ~ categories + movements + (1 | participants) + (1 | motion_coef), data = data, REML = FALSE) modele <- lmer(pupil_dilation ~ categories + (1 | participants) + (1 | motion_coef), data = data, REML = FALSE)

For your information: categories, movements, actors, motion_coef, and participants were converted as factors.

So few questions come to my mind:

- Is LMM a good way to answer my question?
- Do I have to normalize my data before starting my LMM?
- Are the models above seem consistent according to my research question?

I hope I was clear about my description. Also, I am sorry if I didn't explain well about the LMM but as I am new I tried my best!

Thank you all in advance for your precious help!

Camille

https://community.rstudio.com/images/emoji/apple/slight_smile.png?v=12 There was an error displaying this embed.I am a student . these days I am working with JASP. A great software that I use for network analysis for fmri data. I have a problem interpreting my network plots(edges and nodes).Is there any expert person in this field you inform me or any good reference to help me?

AND another question I have : for other methods in JASP there is an option to see the R syntax. but for network analysis, I could not see that option. am I right? or there is R syntax for network analysis?

Thank you in advance and thank you for this great software.

]]>I performed a Bayesian independent samples t-test and also looked at my sequential plot to see how the BF changes with accumulating data. However, till about n = 60, the line remains exactly at BF = 1 (please see below).. Does this make sense?... Would you collect more data when seeing this?

Thank you,

Nathalie