Hi SAS community, I am a novice SAS user. I would like to analyze a dataset from an experiment.  I have tree samples from two trials conducted at different sites. Each site was divided into several blocks, and tree seedlings from 4 origins were randomly planted within each black. 50+ years later, two blocks from each site were selected and 3 trees from each of the origins were harvested. Then each tree was "cut" into wood products using 3 different cutting methods (using a simulation software).  I would like to test if there are differences in product recovery between the two sites, among the origins, and among the cutting methods. 1) I am not interested in blocks, so the block factor will be a random variable. Should I consider trees from each origin as subsampling or as another random factor, or maybe a replication?   2) The analysis I have done so far assumed the trees were randomly select from each origin within each block. What if the trees were randomly selected not at the origin level, but at the block level to cover the full range of tree sizes within the block (tree size have huge effect on recovery)? If this were the case, should I drop origin in the model, because its levels were not considered in the sampling? Here is what I have so far. I am not confident if this is a correct analysis and will be very appreciate if anyone could point me to a correct way to analyze the data (attached).   PROC GLIMMIX data=data; class site origin block cut tree; model recovery = site*origin*cut/ddfm=kr; random block site*origin*cut*block; run;   Thank you very much. Tess ... View more