Generally, the goal of hierarchical modeling is to determine the extent to which factors measured at different levels influence an outcome using a typical regression modeling framework.

Hierarchical data: Data for which there are multiple nested levels of sampling, and/or multiple nested sources of variability. Such data are also often called multilevel data.

Hierarchical models are used for data with some type of grouping structure.

Hierarchical models can have more than two stages. The advantage is that complicated processes may be modeled by a sequence of relatively simple models placed in a hierarchy. …

The use of hierarchical models in analyzing data from experiments and quasi-experiments conducted in field settings. In D. Kaplan (Ed.), The Sage handbook of quantitative …

Entry and exit actions are particularly important and powerful in conjunction with the state hierarchy, because they determine the identity of the hierarchical states

Nov 6, 2012 · In a hierarchical logit model, we simply embed the stochastic linear predictor in the binomial error function (recall that in this case, the predicted mean μ corresponds to the …