which non parametric test to use? friedman or Kruskal? I am conducting a study on textrual complexity. We fed people food (3 types) over 3 sessions and asked questions about hunger levels. 20 participants were tested during 60 trials in total. Of the 14 variables, 4 are non-normal and transformation isn't helping. The other variables were tested using the lmer function in R. As an example:
mod1 <- lmer(H1 ~ g + (1|p), data=pdat)

With H1 being hunger levels and G being which food type and p for participant. We used this to look at fixed and random effects, meaning we can look at the main effect of texture differences in activity while taking into account participant differences, etc.
With the remaining 4 variables we are looking at using non-parametric tests (either a Friedman or Kruskal). The data has equal sizes for each variable.
Which of these tests is the best option?

To perhaps answer the question, tests like Friedman or Quade are used for unreplicated complete block design. This design could be modeled with the formula H1 ~ g + (1|p).
You may want to look into models other than Friedman or Quade, like ordinal regression or aligned ranks transformation anova.
