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Unequal cell design mixed ANOVA in JASP vs. R


I was wondering if someone could clarify for me what assumptions JASP makes for unequal cell designs for mixed-design ANOVAs. I have 2 repeated-measures (Congruency, DemandCue) and two between-subjects measures (Feedback, Experiment). In one experiment, I had N = 60; the next experiment doubled the sample size (N = 120), so Experiment is what has the unequal cell design.

I have been trying to recreate the results that JASP outputs with many different R packages. JASP, for instance, writes that the F statistic for DemandCue is F(1,176) = 26.286, p < 0.001. The F statistic for Congruency is F(1,176) = 147.102, p < 0.001.

After factorizing subject, DemandCue, Experiment, Congruency, and Feedback (and rawRTData <- read.csv('SC_ANOVA_RT.csv') - csv file linked at the bottom), I have tried the following in R:

SC_RT_runANOVA <- aov(RT ~ Feedback * Experiment * DemandCue * Congruency + Error(subject/(Congruency*DemandCue)), data = rawRTData)


AND, using the lme4 package --

anova(lmer(RT ~ (Feedback*Experiment*DemandCue*Congruency) + (1|subject) + (1|DemandCue:subject) + (1|Congruency:subject), data=rawRTData))

These two produce the same result: F(1,176) = 172.1329, p < 0.001 for congruency and F(1,176) = 35.7272, p < 0.001. I saw on the internet that maybe I had to specify contr.sum for the contrasts and type 3 sums of squares, but that did not change the output. I also know that it is the "Experiment" factor at issue here: when I removed it from both the R code and JASP, I was able to reproduce the JASP output with the R code. I also converted the dummy coding of Experiment from 0/1 to E1/E2, and that changed the F stats to 171.9760 for congruency and 35.7607 for DemandCue. So that seemed to me like there are some issues with R handling that I don't get, but that the R peculiarity may not fully explain the JASP/R difference.

I tried looking at the JASP R code, and it had so many dependencies and references to other parts of the code that I found it harder to understand. I also trust JASP more than this simple code, because I think you all have spent more time thinking about how to best to portion out the variance than I have, but I would like to know how to reproduce the JASP results. What underlying assumption am I missing here? Are there actually different assumptions, or is this a merely R peculiarity that I hadn't discovered (e.g., the dummy coding)?

(If you want to use the same long-form data that I mention here, here is a link: The wide-form JASP data is here (the first four columns indicate RM1.1, RM1.2, RM2.1, RM2.2):

Thank you!


  • Hi Chris,

    JASP uses the afex package for (rm)AN(C)OVA.

    Among other things, afex (1) makes sure factors have effect coding, and (2) used type III errors (whereas summary.aov uses type II), both of which affect estimates and significances in unbalanced designs.

    So afex should give the same results as JASP.



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