• 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 ...

Categorical Response Data
Categorical response data
JeroenK.VermntTilburg University, The Netherlands
16.1 Introduction

Earlier chapters in this volume have discussed linear mixed models for continuous responses and generalized linear mixed models for dichotomous variables and counts with binomial and Poisson errors at the lower level, respectively. This chapter deals with multilevel models for discrete response variables with more than two categories; that is, with the situation where these errors can be assumed to come from a multinomial distribution, which can be seen as either a multivariate extension of the binomial distribution or a restricted version of the multivariate Poisson distribution. The dependent variables of interest can have ordered response categories modeled using an ordinal regression model, or unordered response categories modeled using a multinomial logit, a probit, or a ...

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