I am currently studying the effect that a pollutant has on plant growth. The plants come from a few different regions, and it is assumed that plants from the same region share more in common than plants from different regions. Furthermore, from each region we sample a few plants and then take measurements relating to time on them. This means that the repeated measurements from the plants also introduce dependence in the data.
We thus have a multilevel model problem, where plants are nested in regions and also repeated measurements are taken for each plant. (Think of it as one measurement per year per plant.) Furthermore, we have region specific variables (levels of the pollutant, rainfall, temperature, etc) that, within a given year, are shared between all plants in a region. It is the effect of such a region level pollutant on the (individual level) plant growth that is the main interest.
Now, I have read that a good practice is to keep the model maximal when it comes to specifying random effects in linear mixed models (Barr et al. 2013), but I am unsure how to apply this to multilevel models. Should I introduce the random slopes on the individual plant level? Or should they be on the region level since that's the level the variables are measured on? Or perhaps on both?
Specifying a full model on all levels leads to convergence issues (as does specifying a full model on any of the levels), so a truly full model is not an option.
Thankful for advice and/or references to articles where the topic is discussed.
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of memory and language, 68(3): 255-278.