• Summary
  • Contents
  • Subject index

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.

The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference; Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models; Part III includes discussion of missing data and robust methods, assessment of fit and ...

Multivariate Response Data
Multivariate response data
HelenaGeysJanssen Pharmaceutica, Johnson and Johnson, Belgium
ChristelFaesUniversiteit Hasselt, Belgium
21.1 Introduction

The term multivariate refers to the setting where different measurements are taken from the same individual, e.g. the weight and height of a person. Hierarchical or multilevel refers to the setting where the dependent variables correspond to the same measure, e.g. in a clustered setting where measurements are taken from groups of individuals. Methods for multilevel or multivariate data are widely available. The combination of these two, e.g. involving multiple measurements taken repeatedly on an individual, is more challenging. For continuous data, the normal distribution with its elegant properties plays a prominent role. However, when some or all of the outcome variables are non-normal, techniques are less standard, because of the lack ...

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