I have classic block design of the experiment. There are blocks and treatments, and several observation within. The experiment is unbalanced thus I want to use mix models to analyze if the treatment has an effect on several parameters. I plan to have block as a random factor and treatment as a fix.
The thing I do not get is: Shall I use all the data in my data set in the model or shall I calculate means of treatments per block and then use the data in the analysis. I tried both options in R (lmer) and I received slightly different results in estimation of parameters and different variance (which is obviously not surprising).
May I get an explanation which approach shall I use in this case with mean or with raw data?
Maybe I should specify the model in a way that it will account on the several measurements per block and treatment?