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Jason T. Newsom - Longitudinal Structural Equation Modeling: A Comprehensive Introduction

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Jason T. Newsom Longitudinal Structural Equation Modeling: A Comprehensive Introduction
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This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling options are available for which hypotheses. The author demonstrates how SEM is related to other longitudinal data techniques throughout. By exploring connections between models, readers gain a better understanding of when to choose one analysis over another. The book explores basic models to sophisticated ones including the statistical and conceptual underpinnings that are the building blocks of the analyses. Accessibly written, research examples from the behavioral and social sciences and results interpretations are provided throughout. The emphasis is on concepts and practical guidance for applied research rather than on mathematical proofs. New terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues. Each chapter also includes examples of each model type, comment sections that provide practical guidance, model extensions, and recommended readings.

Highlights include:

  • Covers the major SEM approaches to longitudinal analysis in one resource.
  • Explores connections between longitudinal SEM models to enhance integration.
  • Numerous examples that help readers match research questions to appropriate analyses and interpret results.
  • Reviews practical issues related to model specification and estimation to reinforce connections.
  • Analyzes continuous and discrete (binary and ordinal) variables throughout for breadth not found in other sources.
  • Reviews key SEM concepts for those who need a refresher (Ch. 1).
  • Emphasizes how to apply and interpret each model through realistic data examples.
  • Provides the books data sets at www.longitudinalsem.com along with the Mplus and R-lavaan syntax used to generate the results.
  • Introduces the LISREL notation system used throughout (Appendix A).

The chapters can be read out of order but it is best to read chapters 1 4 first because most of the later chapters refer back to them. The book opens with a review of latent variables and analysis of binary and ordinal variables. Chapter 2 applies this information to assessing longitudinal measurement invariance. SEM tests of dependent means and proportions over time points are explored in Chapter 3, and stability and change, difference scores, and lagged regression are covered in Chapter 4. The remaining chapters are each devoted to one major type of longitudinal SEM -- repeated measures analysis models, full cross-lagged panel models and simplex models, modeling stability with state-trait models, linear and nonlinear growth curve models, latent difference score models, latent transition analysis, time series analysis, survival analysis, and attrition. Missing data is discussed in the context of many of the preceding models in Chapter 13.

Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this text also appeals to researchers in these fields. Intended for those without an extensive math background, prerequisites include familiarity with basic SEM. Matrix algebra is avoided in all but a few places.

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First published 2015
by Routledge
711 Third Avenue, New York, NY 10017

and by Routledge
27 Church Road, Hove, East Sussex BN3 2FA

Routledge is an imprint of the Taylor & Francis Group, an informa business

2015 Taylor & Francis

The right of Jason T. Newsom to be identified as the author of this work has been asserted by him in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.

All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers.

Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging in Publication data
Newsom, Jason T.

Longitudinal structural equation modeling : a comprehensive introduction /

Jason T. Newsom.

pages cm. (Multivariate applications series)

1. Longitudinal methodMathematical models. 2. Social sciencesStatistical

methods. 3. Structural equation modeling. I. Title.

H61.25.N49 2015

519.53dc23

2014046412

ISBN: 978-1-84872-696-3 (hbk)
ISBN: 978-1-84872-697-0 (pbk)
ISBN: 978-1-315-87131-8 (ebk)

Typeset in Sabon
by Out of House Publishing

The breadth of topics covered is perfect for my course Connections are - photo 1

The breadth of topics covered is perfect for my course.... Connections are drawn between types of models and their similarities/differences ... [and] ... students often ask questions related to when to use what model.... I am also excited about inclusion of continuous and discrete variables. It is difficult to find texts that include both.... I would absolutely ... recommend it to colleagues and students. I also would consider using this text as a primary textbook for my courses.

Natalie D. Eggum, Arizona State University, USA

Newsom is a particularly gifted writer.... He explains complex material clearly, without over-simplifying it ...The book will be very popular among applied scientists.... [and it is] ... appropriate for graduate courses in either SEM or longitudinal modeling. I would strongly consider adopting this book ... as the primary textbook.

David L. Roth, Johns Hopkins University, USA

I could see this text being ... required for ... SEM ... [in] ... all of the social sciences.... While there are other ... texts on longitudinal analysis, they do not emphasize the use of SEM.... Strengths: ... addresses confusion that [readers] have [when] deciding which analytic technique to choose ... [and includes] analysis on both continuous and discrete variables....The author is a good writer, who can readily explain these advanced statistical topics.

Brian A. Lawton, George Mason University, USA

Many of the current texts on longitudinal data analysis only tangentially mention SEM or do not cover the full spectrum of approaches....This book is really needed in the social science field.... I like the organization ... Newsom starts with the basic issues in longitudinal SEM and moves to more advanced topics through the volume. I also like the way the chapter was formatted with respect to defining terms/concepts and then providing an example with data for each concept.

Kristin D. Mickelson, Kent State University, USA

Multivariate Applications Series

Sponsored by the Society of Multivariate Experimental Psychology, the goal of this series is to apply statistical methods to significant social or behavioral issues, in such a way so as to be accessible to a nontechnical-oriented readership (e.g., non-methodological researchers, teachers, students, government personnel, practitioners, and other professionals). Applications from a variety of disciplines such as psychology, public health, sociology, education, and business are welcome. Books can be single- or multiple-authored or edited volumes that (1) demonstrate the application of a variety of multivariate methods to a single, major area of research; (2) describe a multivariate procedure or framework that could be applied to a number of research areas; or (3) present a variety of perspectives on a topic of interest to applied multivariate researchers.

Anyone wishing to submit a book proposal should send the following: (1) author/title; (2) timeline including completion date; (3) brief overview of the books focus, including table of contents and, ideally, a sample chapter (or chapters); (4) a brief description of competing publications; and (5) targeted audiences.

For more information, please contact the series editor, Lisa Harlow, at Department of Psychology, University of Rhode Island, 10 Chafee Road, Suite 8, Kingston, RI 02881-0808; phone (401) 874-4242; fax (401) 874-5562; or e-mail LHarlow@uri.edu.

What If There Were No Significance Tests? co-edited by Lisa L. Harlow, Stanley A. Mulaik, and James H. Steiger (1997)

Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming, written by Barbara M. Byrne (1998)

Multivariate Applications in Substance Use Research: New Methods for New Questions, co-edited by Jennifer S. Rose, Laurie Chassin, Clark C. Presson, and Steven J. Sherman (2000)

Item Response Theory for Psychologists, co-authored by Susan E. Embretson and Steven P. Reise (2000)

Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, written by Barbara M. Byrne (2001)

Conducting Meta-Analysis Using SAS, written by Winfred Arthur, Jr., Winston Bennett, Jr., and Allen I. Huffcutt (2001)

Modeling Intraindividual Variability with Repeated Measures Data: Methods and Applications, co-edited by D. S. Moskowitz and Scott L. Hershberger (2002)

Multilevel Modeling: Methodological Advances, Issues, and Applications, co-edited by Steven P. Reise and Naihua Duan (2003)

The Essence of Multivariate Thinking: Basic Themes and Methods, written by Lisa Harlow (2005)

Contemporary Psychometrics: A Festschrift for Roderick P. McDonald, co-edited by Albert Maydeu-Olivares and John J. McArdle (2005)

Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming, Second Edition, written by Barbara M. Byrne (2006)

A Paul Meehl Reader: Essays on the Practice of Scientific Psychology, co-edited by Niels G. Waller, Leslie J. Yonce, William M. Grove, David Faust, and Mark F. Lenzenweger (2006)

Introduction to Statistical Mediation Analysis, written by David P. MacKinnon (2008)

Applied Data Analytic Techniques for Turning Points Research, edited by Patricia Cohen (2008)

Cognitive Assessment: An Introduction to the Rule Space Method, written by Kikumi K. Tatsuoka (2009)

Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Second Edition, written by Barbara M. Byrne (2010)

Handbook of Ethics in Quantitative Methodology, co-edited by Abigail T. Panter and Sonya K. Sterba (2011)

Longitudinal Data Analysis: A Practical Guide for Researchers in Aging, Health, and Social Sciences, co-edited by Jason T. Newsom, Richard N. Jones, and Scott M. Hofer (2012)

Structural Equation Modeling with MPlus: Basic Concepts, Applications, and Programming written by Barbara M. Byrne (2012)

Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis, written by Geoff Cumming (2012)

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