I have repeated observations (count data) conducted in different plots (say, 5 times each plot). I would like to conduct a regression analysis of the observation data at the plot level with plot-level independent variables. I'd like to consider approaches besides putting the plot-level independent variables into an observation-level model because I plan to build a structural equation model that will relate to other datasets at the plot level.
I could average the repeated observations for each plot, but each observation has individual sources of variability (e.g. weather conditions, time of day, surveyor ID, etc.) that could be systematically affecting the data, so I would like to factor them out.
Would it be possible to remove these nuisance sources of variability by either:
- Making an observation-level mixed-effects model with plot as a random effect grouping and running the plot-level analysis on the fitted random effects coefficients.
- Fitting an observation-level model without mixed effects and averaging the residuals of this model by plot groupings and running the plot-level analysis on the averaged residuals.
Are either of these an appropriate approach? Or is there a more recommended or standard approach that I'm naively not aware of?