I'm currently working with data which has continuous variables and a hierarchical structure attached to it, think of measuring blood pressure, size and weight of different domestic animals (cats, dogs, birds) as well as of their species, family and order.
All data is measured on the level of the individuals, so there are no predictors on higher levels (although they could be generated by taking, e.g. the inter-level mean).
Let's say I want to predict the blood pressure (y) with the help of the weight (x1) and the size (x2).
Ignoring the hierarchical information, I could use a linear model y=β0+x1β1+x2β2, which might be a very bad idea.
What might be the right approach for dummy variables if there are more than two categories?