Hi,

Use a nested anova (also known as a hierarchical anova) when you have one measurement variable and two or more nominal variables. The nominal variables are nested, meaning that each value of one nominal variable (the subgroups) is found in combination with only one value of the higher-level nominal variable (the groups). All of the lower level subgroupings must be random effects (model II) variables, meaning they are random samples of a larger set of possible subgroups.

Nested analysis of variance is an extension of one-way anova in which each group is divided into subgroups. In theory, these subgroups are chosen randomly from a larger set of possible subgroups.

For example, let's say you are testing the null hypothesis that stressed and unstressed rats have the same glycogen content in their gastrocnemius muscle. If you had one cage containing several stressed rats, another cage containing several unstressed rats, and one glycogen measurement from each rat, you would analyze the data using a one-way anova. However, you wouldn't know whether a difference in glycogen levels was due to the difference in stress, or some other difference between the cages, maybe the cage containing stressed rats gets more food, or is warmer, or happens to contain a mean rat who uses rat mind-control techniques to enslave all the other rats in the cage and get them to attack humans.