• 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 ...

Hierarchical Dynamic Models
Hierarchical dynamic models
MarinaSilvaPaez and DaniGamermanUniversidade Federal do Rio de Janeiro, Brazil
19.1 Introduction

Consider a population of students divided into groups (schools or classes), for which we believe there are similarities (about a certain aspect of interest) within students in the same group. More specifically, suppose we are interested in analyzing the proficiency in maths of students attending at the same level at different schools, and all of them are submitted to the same test. It is reasonable to expect that the average score obtained by the students is not the same at every school, as characteristics of the school could possibly be affecting this average. For this specific example, the teaching skills of the maths teacher could be an important aspect. Other variables ...

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