I’m new here so please let me know if I am missing anything in this description/explanation.
I have a 4x4 repeated measures design. My dependent variable is pupil dilation, my two IVs are light level and signal-to-noise ratio (target sentence to background noise). I have 3 random effects: participant, sentence, and trial number. See a snapshot of dataframe below
I have built the model according to the “keeping it maximal” advice in “Random effects structure for confirmatory hypothesis testing: Keep it maximal” by Barr et al (2013). So I have fit the most complex model consistent with the experimental design, removing only terms required to allow a non-singular fit (Barr et al. 2013)
This is my model:
lmer(peakdilation ~ snr * lightlevel + (1 + lightlevel|participant) + (1|trial_exp) + (1|sentence), pup_data, REML = FALSE, control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e9)))
I am really struggling to figure out the best course of action…
• I have looked at log transforming but know this isn’t always the best option as it can make interpretation tricky (also, I had to add 1 to make some numbers positive and after that, using log(peakdilation) in my model did fully not solve the issue).
• I have looked at switching to nlme but have read that they are not particularly suitable for fully-crossed designs
• I have looked at using glmer but am finding it hard to know how to specify the model to account for this heteroskedasticity
• I have looked at using rlmer from robustlmm but again finding it hard to specify and documentation doesn’t seem to explicitly mention heteroskedasticity.
• I have looked at using brms but I am not very familiar with the Bayesian framework at this stage (and currently time-pressured)
Feeling a bit overwhelmed with the amount of information and all the different methods out there. Could anyone here please offer any advice (in accessible language) about how to move forward and what to do?
Thanks very much in advance