Multilevel Statistical Models [NOOK Book]

Overview

Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how ...
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Multilevel Statistical Models

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Overview

Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models.

Key Features:

  • Provides a clear introduction and a comprehensive account of multilevel models.
  • New methodological developments and applications are explored.
  • Written by a leading expert in the field of multilevel methodology.
  • Illustrated throughout with real-life examples, explaining theoretical concepts.

This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.

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Editorial Reviews

Booknews
Describes the application of systematic approaches to the statistical modeling and analysis of hierarchically structured data, and the core set of techniques and software packages that have been developed since the mid 1980s to apply them in education, epidemiology, geography, child growth, household surveys, and other areas. The second edition integrates new developments into the framework and terminology of the first, which was published in 1987 as Multilevel Models in Educational and Social Research by Edward Arnold, London. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher

“This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial. See the review of the third edition.” (Zentralblatt MATH, 1 December 2013)

"This book would also serve as an outstanding general reference on multilevel models, since it offers concise and easy to follow descriptions of the various multilevel models and their applications, in addition to the references on which this work is based. I really enjoyed reading this book, and am sure that others will have a similar pleasurable experience." (Journal of Biopharmaceutical Statistics (JBS), 2012)

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Product Details

  • ISBN-13: 9781119956822
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 7/8/2011
  • Series: Wiley Series in Probability and Statistics , #922
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 4
  • Pages: 382
  • File size: 13 MB
  • Note: This product may take a few minutes to download.

Table of Contents

Preface
Acknowledgements
Notation
Glossary
1 Introduction 1
2 The Basic Linear Multilevel Model and its Estimation 15
3 Extensions to the Basic Multilevel Model 47
4 The Multivariate Multilevel Model 69
5 Nonlinear Multilevel Models 77
6 Models for Repeated Measures Data 87
7 Multilevel Models for Discrete Response Data 97
8 Multilevel Cross Classification 113
9 Multilevel Event History Models 125
10 Multilevel Models with Measurement Errors 141
11 Software for Multilevel Modelling; Missing Data and Multilevel Structural Equation Models 153
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