Marginal Models For Dependent, Clustered, and Longitudinal Categoric
Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answ
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Wicher Bergsma • Marcel Croon Jacques A. Hagenaars
Marginal Models For Dependent, Clustered, and Longitudinal Categorical Data
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Wicher Bergsma London School of Economics Department of Statistics Houghton Street London, WC2A 2AE United Kingdom [email protected]
Jacques A. Hagenaars Tilburg University Fac. of Social & Behavioural Sciences Department of Methodology PO Box 90153 5000 LE Tilburg The Netherlands [email protected]
Marcel Croon Tilburg University Fac. of Social & Behavioural Sciences Department of Methodology PO Box 90153 5000 LE Tilburg The Netherlands [email protected] Series Editors: Stephen Fienberg Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 USA
Wim J. van der Linden CTB/McGraw-Hill 20 Ryan Ranch Road Monterey, CA 93940 USA
ISBN 978-0-387-09609-4 e-ISBN 978-0-387-09610-0 DOI 10.1007/978-0-387-09610-0 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009922353 c Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This book has been written with several guiding principles in mind. First, the focus is on marginal models for categorical data. This is partly done because marginal models for continuous data are better known, statistically better developed, and more widely used in practice — although not always under the name of marginal models — than the corresponding models for categorical data (as shown in the last chapter of this book). But the main reason is that we are convinced that a large part of the data used in the social and behavioral sciences are categorical and should be treated as such. Categorical data refer either to discrete characteristics that are categorical by nature (e.g., religious denominations, number of children) or result from a categorical measurement process (e.g., using response categories such as Yes versus No, Agree versus Neither Agree nor Disagree versus Disagree). Treating categorical data as realizations of continuous variables and forcing them somehow into models for continuous data always implies making untestable assumptions about the measurement process and about underlying conti
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