Multilevel Modeling UsingR

Multilevel Regression and Multilevel Structural Equation Modeling

Multilevel modeling in general concerns models for relationships between variables defined at different levels of a hierarchical data set, which is often viewed as a multistage sample from a hierarchically structured population. Common applications are individuals within groups, repeated measures within individuals, longitudinal modeling, and cluster randomized trials. This chapter treats the m...

Distributed Multilevel Modeling

Multilevel modeling is a popular statistical technique for analyzing data in hierarchical format, and thus naturally fits within a distributed database framework. We consider the computational aspects of multilevel modeling across distributed databases. In addition, we consider these aspects under a generalization of the multilevel model where the distributed groups (or databases) are allowed t...

Multilevel Structural Equation Modeling

Iterative numerical procedures for the estimation of variance and covariance components for unbalanced designs were developed in the 1980s and were implemented in software packages such as MLWIN, SAS PROC MIXED and HLM. At the same time, interest in latent variables, that is, variables that cannot be directly observed or can only imperfectly be observed, led to the theory providing for the defi...

Multilevel Structural Equation Modeling

In conventional structural equation models, all latent variables and indicators vary between units (typically subjects) and are assumed to be independent across units. The latter assumption is violated in multilevel settings where units are nested in clusters, leading to within-cluster dependence. Different approaches to extending structural equation models for such multilevel settings are exam...

Sample Sizes for Multilevel Modeling