Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure. These Multilevel Factor Models (MFMs) have in common that-as in multilevel regression ana...

Multilevel modeling is a technique that has numerous potential applications for social and personality psychology. To help realize this potential, this article provides an introduction to multilevel modeling with an emphasis on some of its applications in social and personality psychology. This introduction includes a description of multilevel modeling, a rationale for this technique, and a dis...

Surface wavelet representations [1] are a generalization of classical wavelets on the real line. They allow us to build a multilevel model of spatial data that is defined on an irregular mesh describing a general domain. Such wavelet representations differ from the classical case in that the wavelet filters are spatially variant. Using surface wavelets, one can develop a biorthogonal geometry-a...

Workshop in Norwich, England A three day workshop on multilevel modelling will be held at the University of East Anglia from 8-10 January 1997. It will be assisted by a team from the Multilevel Models Project and will give participants the opportunity to see a preliminary version of MLn for Windows. For further information contact Anne-Lise McDonald at Conference and Course in The Netherlands A...

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...