Hierarchical Modeling
Jan de Leeuw
UCLA
Statistics
In this presentation, we outline the hierarchical modeling, or multilevel, approach to the analysis of fMRI data. In this framework, preprocessed fMRI data are collected under various conditions or across various groups of individuals. Each group or condition is assigned its own dedicated general linear model, in which the regression coefficients for the various groups serve as outcomes in a between-group regression model that links the separate within-group regression models.
We provide a general introduction to linear two-level modeling and then extend the discussion to generalized linear models, multivariate responses, models with more than two levels, and more complex error structures. Estimation methods and computer software are discussed briefly, and some of the available software is applied to preprocessed fMRI data.