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RM ANOVA different results in JASP and R.

Dear JASP team,

I am running a RM three way ANOVA in R using:

anova_test(data, dv=A, wid=subject, within=(c(B,C,D))

This gives me slightly different results for some factors than running a RM in JASP e.g

Factor A: F= -1.02e-12 (on R) vs. F=2.878 x 10^-5 (JASP)

but for other factors it gives the same result e.g

Factor C: F= 1.7636 e+01 (on R) vs. F = 17.636 (on JASP)

Why is that so? some of the interaction F values are exactly the same whereas others differ.

Then, as I wanted to follow up on a significant A X C interaction running the following on R:

data %>%

group_by(A,C,subject) %>%

get_summary_stats(values, type='mean_ci') %>%

group_by(A) %>%

pairwise_t_test(mean ~ C, paired=TRUE)

This output gives the same as grouping my variables and averaging them and just running a paired t-test on JASP. However, it does not output the same as if I use the post-hoc default on JASP as a follow-up.

Why these differences?



  • Factor A: F= -1.02e-12 (on R) vs. F=2.878 x 10^-5 (JASP)

    Hmm negative F-test isn't even possible...

  • Hi there,

    I would like to investigate this issue further, would you mind sharing the data here?


    Jonas (JASP-team)

  • Hi Jonas and patc3 thanks for your comments, I had not even noticed the negative distribution.

    As I was prepping the files to share, I solved the issue.

    It seems like there is something wrong with this particular file when writing it with matlab with:


    If I just open the file on excel and re-save it as a .csv now the R code and JASP give the same results.

    Any ideas as to why this may have happened / suggestions on how to avoid it? It's very odd! / Does not happen with any of the other files I am saving in the exact same way.

    Thanks very much

  • Sorry, the above fixes the ANOVA results.

    However, for the post-hoc t-tests, how is JASP calculating the post-hoc tests for an interaction vs. doing a paired samples t-test for the conditions of interest (averaging across the other factors)?

    Both approaches still give slightly different t-values (but not sure if may be due to rounding errors or slightly different computations).

    Still happy to share the data if still necessary.

    Thanks very much

  • edited April 24

    Hi @albapy ,

    Sorry for the late reply. You can read about this behavior in this recent discussion: and linked blogpost:

    Basically, the marginal means (which are estimated based on the specified model) can differ from the observed group means. Since these are used for the followup analyses (contrasts/posthoc), the results can differ.



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