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?

Answer
It seems based off your comments that you are only removing these predictors from the full mixed model because they are non-normal. Yet they seem theoretically relevant to your research question.
Thus, the correct answer to your question is that none of these tests are useful for your purpose, because you are trying to model how these all control for the predicted outcome. Linear mixed models do not assume normality for predictors. They only assume normality of residuals. See the below two citations, which cover what assumptions you need to meet in mixed models.
Citations

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*Harrison et al., 2018

*Meteyard & Davies, 2020
