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How to set up a Bayesian RM ANOVA?

edited May 2016 in JASP & BayesFactor


this is a seemingly simple question. But I don't quite understand JASP's GUI. I want to run a simple repeated-measures ANOVA. I am working on a pre-registration and want to make sure that I know exactly how to do the analysis. I have simulated some data that I can use in JASP (script below).

Essentially, I have one outcome measure (parameter), one two-level within-subject factor (reward) and two factors that I'd like to add as "nuisance": subj and item. My question is whether there's a difference in parameter values between high and low reward (taking variation between subjects and items into account). I'd like to have the inclusion Bayes factor for reward.

The JASP Bayesian RM ANOVA GUI gives me three fields:

  • "repeated measures cells": I feel like this is where reward should go but it won't let me add it.
  • "between subject factors": I don't have any, right?
  • "Covariates": I guess I put subj and item here and then mark them as "nuisance" in the "Model" dropdown menu below?

I don't get how to set my DV (parameter) and my within-subject factor (reward) properly.

Any help is appreciated!

  • Florian

Script to generate data:

n <- 30

header <- c("subj", "item", "reward", "parameter")

data <- NULL

for(s in 1:n) {
  n.items <- round(runif(1, 20, 50))

  hr <- rbinom(n.items, 1, .5)
  hr <- ifelse(hr == 1, "high", "low")
  par <- abs(rnorm(n.items, .3, .1))

  tmp <-, n.items), 1:n.items, hr, par))
  colnames(tmp) <- header
  data <- rbind(data, tmp)

write.csv(data, "~/Desktop/simulated data.csv", row.names = FALSE)


  • edited 8:43AM

    JASP expects a wide format instead of long format. Then simply fill out the Factors table (with category and levels) and fill the cells with the corresponding columns.

  • floflo
    edited 8:43AM


    thanks for your response! I played around with converting the data to wide format but I am not quite sure how to do exactly what I intended to do.

    In the simulated data (see above), I have one parameter value for each item for each participant. Each item can be associated with either a high or a low reward. I want to know whether there is an effect of reward-level on the parameter value. If I generate data with the script above, I can then do:

    foo <- aggregate(parameter ~ subj + reward, data, median) # across items
    wide <- spread(foo, reward, parameter) # from library(tidyr)

    When I read that data into JASP, everything works nicely: I can set the high/low reward as the "repeated measures cells".

    However, that way, I lose all the information about the items - because I aggregated across them now. If I don't do that, though, JASP throws an error because the number of rows are not the same (not every subj saw the same number of items). So, if I use spread() on data directly (wide <- spread(data, reward, parameter)) and read that information into JASP, I can't work with it.

    I see that JASP automatically includes subj in the model. Is there a way to also include item as a nuisance? (Because I know that not all items are equally difficult and it'd be nice to take that into account.


    • Florian
  • edited June 2016

    Hi Florian,

    Edit: I just realized that you're talking about a Bayesian RM ANOVA, and not a traditional one. I suspect that everything below holds in both cases, but I may be wrong. @EJ?

    If I understand your question correctly, what you want to do is not possible with a repeated measures ANOVA, not in JASP nor in any other package. A RM ANOVA always has a single random effect, which can be either item or subject, and these are put in different rows. (That is, your cells contain either averages across items, or averages across subjects.) If you have two relevant random effects (as is often the case in psycholinguistics for example, when working with items and subjects), the tradition is to run two RM ANOVAs, one with subject as random effect (F1), and the other with item as random effect (F2), and report both.

    If you want to include more than one random effect, I think the best solution is to use a linear mixed-effects model. This is not possible in JASP, but it's fairly easy in R with the lme4 package:

    I hope this helps!


  • edited 8:43AM

    The Bayesian "repeated measures ANOVA" in JASP is not really a "repeated measures ANOVA"; it is only called that so that people won't get confused. In the background it is using the BayesFactor package, which implements Bayesian linear mixed effects models, in which it is possible to have crossed random effects.

    So, contrary to what Sebastiaan suggested, crossed random effects are perfectly possible. As for the details of how to do this in JASP, I'm not sure; someone more familiar with the interface would have to say.

  • edited 8:43AM

    @richarddmorey Thanks, that's very useful to know! I actually wanted to say hi in Granada after the methods session. But the discussion went on for so long, and I had to leave!

    But I don't think it changes the story for JASP though. JASP uses more-or-less the same interface for the Bayesian RM as for the traditional RM, and this requires the data to be in so-called wide format, in which each participant (or item) is on one row and different conditions are in different columns. So I don't see how you can do crossed random effects in JASP, even though the underlying R package is apparently able to do it.

    @lvanderlinden made a video showing how to set up the data for a RM in JASP. It's French, and for a regular (non-Bayesian) RM, but it's very clear, and it may still be useful:


    And for those who are not familiar with all this statistical jargon, here is my best understanding of a few terms (and how they are used in this context, because different people use them differently):

    • A random effect is something that you vary, but you assume that each variation is a random sample of some underlying population. Participant and item are the best examples, because you assume that it shouldn't matter which participants and items you use.
    • A fixed effect is something that you expect to have an effect. For example, experimental conditions.
    • If all combinations of random effects occur, you call them crossed random effects. For example, if all participants see all items, then these are crossed random effects. Random effects are usually crossed, in other words.
  • floflo
    edited 8:43AM

    Hi Richard,

    thanks for the response. I ended up doing it in R using the BayesFactor package and then just made my own plots etc.

    • Florian
  • edited 8:43AM

    Hi everyone,

    I have experimental data that I want to analyse using Bayes ANOVA. My design is repeated measures with two factors. Please can someone talk me through how to do this? Also I noticed there is new Bayesian extension commands for SPSS, however it appears this is restricted to between-group design. Is there alternative user friendly software that I can use to run the Bayes RM analysis?

    Thanks in advance for your response, guidance and support.


  • EJEJ
    edited 8:43AM

    Hi Tom,

    I'm currently working on a paper that explains how to do this. We will post some videos too. These are not available right at the moment, and I'd encourage you to check out this paper:

    The way the interface works is similar to the classical analysis in SPSS.

  • edited 8:43AM

    Hi EJ,

    Many thanks for your reply and apologies for my late reply. I have now started using JASP and it is excellent. I also find the paper very useful, thanks. I think JASP is much preferred to SPSS. I have ran the Bayes RM analysis on JASP, but I'm still stuck with how to interpret the output. Please is there a documentation available somewhere that explains this?

    Thanks in anticipation of your reply.


  • edited 8:43AM

    Below is my output:

    Bayesian Repeated Measures ANOVA

    Model Comparison - dependent
    Models P(M) P(M|data) BF M BF 10 % error
    Null model (incl. subject) 0.200 4.773e -9 1.909e -8 1.000
    Condition 0.200 4.341e -8 1.736e -7 9.095 1.178
    Setsize 0.200 0.002 0.008 433982.994 1.130
    Condition + Setsize 0.200 0.206 1.038 4.316e +7 1.528
    Condition + Setsize +
    Condition  ✻  Setsize 0.200 0.792 15.226 1.659e +8 0.860

    Note. All models include subject.

    Analysis of Effects - dependent
    Effects P(incl) P(incl|data) BF Inclusion
    Condition 0.600 0.998 321.19
    Setsize 0.600 1.000 1.384e +7
    Condition  ✻  Setsize 0.200 0.792 15.23

    Please could you kindly advise what this output means. Please note each of the two factors (condition and set size) has three levels. Thanks in advance.


  • edited 8:43AM



  • edited 8:43AM

    Hi EJ,

    Please find below my output that needs interpreting. Please kindly advise.



  • EJEJ
    edited 8:43AM

    Hi Tom,

    Well, if you look at BF10, you see that for every model you have strong evidence in its favor compared to the model without any factors. The full model that includes the interaction is supported (versus the null) more than the model with the two main effects. If you want to get the BF for the inclusion of the interaction you can either divide the two BF10s, or you can add the main factors "as nuisance" to the null model. This yields 1.659e8/4.316e7 = 3.84. This is some evidence, but it isn't overwhelming (!).

    The effects option computes the factor inclusion probabilities by averaging across the models. You can see you have good evidence for including the interaction (BF=15). The difference between the 3.84 and the 15 is because you are asking a different question. In the case of the 3.84, you are comparing the full model against a pretty good alternative (i.e., the two main effect model); in the case of the BF=15, all model are considered, also the ones that happen not to do very well. I would simply report all of these results in a transparent manner.


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