[R-sig-ME] Index-terms confusion
john.maindonald at anu.edu.au
Fri Dec 24 01:37:51 CET 2010
A strict use of language would use the term "hierarchical multilevel model" when the error terms have a hierarchical structure, e.g. subplots within
plots within blocks within sites. One may also speak of a "hierarchical structure of variation". In practice, "hierarchical multilevel model" is likely, in a context where multilevel models are in mind, to be abbreviated to "hierarchical model".
In other contexts, there can be other hierarchies. Where there is a sequence of models in which each model is nested in the next (in the
sense that its terms are a subset of those in the next models, one may speak of this as a hierarchy of models.
You ask "How about statistics that should inform about changes in betas?" I do not understand the intent of this question.
John Maindonald email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
On 24/12/2010, at 2:42 AM, Petar Milin wrote:
> Hello all!
> I noticed that many users, here and otherwise, use term "hierarchical models" to refer to models that are nested. However, in other places, one can find "hierarchical models" to refer to the "multilevel models". There, one determine direct/indirect relations of some predictors with the criterion, aiming in particular to understand mediator effects (significance of changes in betas of one variable when another intervene etc.). Recently, I realized that some of my students are quite confused with this terminology. How would you explain commonalities and differences between the two? I know about nlme plus multilevel packages for multilevel regression (http://cran.r-project.org/doc/contrib/Bliese_Multilevel.pdf). Any other recommendation? How about statistics that should inform about changes in betas? Some advices for practitioners?
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