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EDIT:

The majority of papers in this field rely heavily on good ole' n-way repeated measures ANOVA's. I noticed that when cleaning the data the underlying distributions for the participants were all over the place and I was going to chuck away that variability when I aggregated their scores the way normal fixed effects-only ANOVA's require me to. I read somewhere that this might lead to an increase in type 1 errors. I don't know if this illustrates the point...

When running a garden variety 3 way repeated measures ANOVA:

                Df Sum Sq Mean Sq F value              Pr(>F)    
load             1   12.0  12.014 103.679 <0.0000000000000002 ***
comp             1    0.3   0.259   2.237              0.1349    
sal              1    0.0   0.014   0.117              0.7320    
load:comp        1    0.1   0.052   0.451              0.5017    
load:sal         1    0.0   0.017   0.148              0.7000    
comp:sal         1    0.1   0.147   1.268              0.2601    
load:comp:sal    1    0.5   0.489   4.224              0.0399 *

The maximal MLM:

              Df  Sum Sq Mean Sq  F value
load           1 11.2885 11.2885 116.0419
comp           1  0.0704  0.0704   0.7236
sal            1  0.0090  0.0090   0.0930
load:comp      1  1.0643  1.0643  10.9405
load:sal       1  0.0282  0.0282   0.2900
comp:sal       1  0.0003  0.0003   0.0032
load:comp:sal  1  0.2287  0.2287   2.3506

The main interaction of interest is usually the two way interaction between load and comp, or, in my case, the three way interaction.

I have a data from a 2 x 2 x 2 full factorial repeated measures experiment and I'm trying to fit a linear mixed effects model to it, which excites me to no end.

I have a data from a 2 (load) x 2 (comp) x 2 (sal) full factorial repeated measures experiment and I'm trying to fit a linear mixed effects model to it. Here is a sample of the data:

 id   load         comp        sal        order  rt_in     
 12   High      Neutral     Non_Salient   178    0.6666667 
  3   High   Incompatible     Salient     127    0.6666667 
 14   High   Incompatible   Non_Salient    38    0.6671114 
  1   High   Incompatible   Non_Salient    58    0.6743088 
  6   High   Incompatible     Salient       7    0.6743088 
  1   High   Incompatible   Non_Salient   119    0.6743088 
  4   High      Neutral     Non_Salient    57    0.6743088 
  7   High   Incompatible   Non_Salient    62    0.6743088 
 20   High      Neutral       Salient      62    0.6811989 
 18   High      Neutral     Non_Salient   169    0.6816633 

There are 19 participants that each completed 194 trials, corresponding to 24 trials of each unique factor combination. The response times were inverse transformed.

Or would something like this be more appropriate?

model_10<-lmer(rt_in ~ 1 + 
         (1|load) + (1|comp) + (1|sal) + (1|load:comp) +
         (1|load:sal) + (1|comp:sal) + (1|load:comp:sal) + (1|id),    
          data = main_data, REML = FALSE)