Handbook of Structural Equation Modeling

754 Pages·2012·16.51 MB·English

by  Rick H. Hoyle

ebook
THE GUILFORD PRESS

Handbook of Structural Equation ModEling

Editorial advisory board

David Kaplan, PhD, University of Wisconsin–Madison

George A. Marcoulides, PhD, University of California, Riverside

Stephen G. West, PhD, Arizona State University

Handbook of
Structural
Equation
ModEling

EditEd by
RiCK H. Hoyle

tHE guilford PrESS
new York london

© 2012 The Guilford Press
A Division of Guilford Publications, Inc.
72 Spring Street, New York, NY 10012
www.guilford.com

All rights reserved

No part of this book may be reproduced, translated, stored in a retrieval system,
or transmitted, in any form or by any means, electronic, mechanical, photocopying,
microfilming, recording, or otherwise, without written permission from the publisher.

Printed in the United States of America

This book is printed on acid-free paper.

Last digit is print number: 9 8 7 6 5 4 3 2 1

Library of Congress Cataloging-in-Publication Data

Handbook of structural equation modeling / edited by Rick H. Hoyle.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-60623-077-0 (hardback : acid-free paper)
1. Structural equation modeling. I. Hoyle, Rick H.
QA278.3.H36 2012
519.5—dc23

2011039804

 v

Structural equation modeling (SEM) has come of age. As recently as the 1980s, SEM was perceived
by many students and researchers in the social and behavioral sciences as virtually intractable— esoteric
notation, difficult-to-use computer programs, and little published guidance targeted to would-be users
with basic graduate-level training in statistical methods. The traditional LISREL notation system is
now more familiar to many students and researchers, and alternative, more intuitive systems have been
developed. Whereas there was once only LISREL for mainframe computers, there now are multiple
computer programs for implementing SEM that run on desktop computers using syntax that does not
require knowledge of matrix algebra. And one could now fill a shelf with textbooks and edited volumes
devoted to SEM and SEM-related topics. A statistical approach that once was accessible only to social
and behavioral scientists with advanced coursework in statistical methods and strong computing skills
is now part of the methodological mainstream.
Despite the growing literature on SEM targeted to graduate students and researchers, there has,
to date, been no single resource that offers broad and deep coverage of both the mechanics of SEM
and specific SEM strategies and applications. This handbook is that resource. If offers comprehensive
coverage of SEM, beginning with background issues, continuing through statistical underpinnings and
steps in implementation, then moving into basic and advanced applications of SEM. In a single volume,
it offers virtually complete coverage of SEM and its use.
The book is intended for advanced graduate students and postgraduate researchers with graduate-
level training in applied statistical methods that include multiple regression analysis and at least basic
coverage of factor analysis. The structure of the book, described below, is designed to lead readers from
basic, foundational material through coverage of the increasing number of modeling approaches and
model types for which SEM is appropriate. As such, the book could serve as the primary textbook for
a graduate-level course on SEM. Alternatively, it could serve as a resource for students and researchers
who have completed their statistical training but need to know more about how SEM works and how it
could be used in their work. In either case, the goal is to provide coverage at a level suitable for gradu-
ate students and postgraduate researchers who have had basic statistical training typical of the social
and behavioral sciences.
To that end, the authors, of whom many are at the forefront of developments related to the topic
about which they have written, were challenged with producing focused chapters that balance sophis-

Preface

vi 
Preface

tication and accessibility. The level of sophistication necessarily varies but, generally, increases from
early to later chapters. Some chapters in the last part of the book cover highly specialized applications
at a level that assumes a solid grasp of the statistical underpinnings of SEM. Yet, even in these chapters,
the authors have provided conceptually oriented descriptions and revealing examples. Many of the
chapters offer fully explicated analyses, including access to data and syntax files for readers interested
in trying their hand at reproducing the authors’ results. (These can be accessed at the website for the
Handbook: www.handbookofsem.com.) The result is a set of chapters that provide up-to-date, acces-
sible, and practical coverage of the full array of SEM topics.
The 40 chapters are arrayed in five parts designed to move the reader from foundational material
through the statistical underpinnings and practicalities of using SEM, to basic and advanced applica-
tions. The chapters in Part I provide important background, beginning with a historical account of key
advances and including material on path diagrams, latent variables, causality, and simulation methods.
Part II is the “nuts-and-bolts” portion of the book, comprising chapters on assumptions, specifica-
tion, estimation, statistical power, fit, model modification, and equivalent models. Also included is a
chapter on the use of categorical data in SEM. Part III, a practically oriented “how-to” portion of the
book, covers preparing data, managing missing data, bootstrapping, choosing computer software, and
writing the SEM research report. Parts IV and V cover the many types of models and data for which
SEM is appropriate. Part V includes chapters on “basic” applications—those that have been in use for
the longest period of time and/or serve as building blocks for newer, more complex or specialized ap-
plications. These include confirmatory factor analysis; models of mediation and moderation; models
of longitudinal data; models focused on means; models for the construction and development of mea-
surement scales; and models for evaluating measurement equivalence for different populations. Part V
includes a dozen chapters that showcase the newest and most specialized SEM models and modeling
strategies. Some chapters focus on the use of SEM to model data generated by relatively new methods
such as brain imaging, genotyping, and geocoding. Others cover strategies for more general types of
data that pose particular challenges but offer unique opportunities; these include multilevel data, cat-
egorical measurement data, longitudinal growth data, data from intensive longitudinal assessments, dy-
adic data, and data from heterogeneous samples for which the source of heterogeneity is not observed.
Also included in Part V are chapters on emerging strategies—Bayesian methods and automated model
specification.
Together, these parts form a coherent whole that provides comprehensive, in-depth, coverage of
SEM in a style appropriate for advanced graduate students and researchers in the social and behavioral
sciences.

 vii

My own introduction to SEM was provided by Kenneth A. Bollen, a master teacher whose classic
textbook has been the source of information about SEM for a generation of graduate students in the
social and behavioral sciences. I had the good fortune of taking Ken’s graduate seminar on SEM at the
University of North Carolina at Chapel Hill in 1987. It was a privilege that continues to pay professional
dividends, including the publication of this handbook.
As the structure and likely content of the Handbook took shape, it became apparent that, if it
were to be of the highest quality, I would need assistance recruiting authors and helping them shape
their contributions. This assistance was provided by three leading scholars on SEM, who served on
the advisory board. The outstanding slate of contributors and the overall quality of the book owe in no
small measure to their contributions. I thank Advisory Board members David Kaplan (University of
Wisconsin–Madison), George A. Marcoulides (University of California, Riverside), and Stephen G.
West (Arizona State University). They helped finalize the list of topics to be covered, recruit authors,
and manage the review of selected chapters. These are critical contributions for which I am most thank-
ful.
The lead authors of most chapters reviewed and provided feedback on another chapter in the
book. In addition, several individuals who were not contributors to the Handbook reviewed selected
chapters. I gratefully acknowledge the efforts of Hei Ning Cham (Arizona State University), Erin K.
Davisson (Duke University), Amanda Gottschall (Arizona State University), Wolfgang M. Hartmann
(Heidelberg, Germany), Patrick L. Hill (University of Illinois), Karin Schermelleh-Engel (University
of Frankfurt), and Jodie Ullman (California State University, San Bernardino). Their incisive reviews
and constructive feedback helped selected authors strengthen and polish early drafts.
Working with the staff at The Guilford Press has been a genuine pleasure. C. Deborah Laughton,
Publisher of Guilford’s Methodology and Statistics program, with whom I’ve now had the privilege
of developing three books, is without peer. Her wisdom, experience, and commitment to the project
were key to its development and completion. In addition, members of the production staff at Guilford
handled the challenging manuscript with skill, turning a complex set of chapter manuscripts delivered
in various states of (in)consistency with guidelines into a polished final product. The contributors and
I owe a debt of gratitude to Editorial Assistant Mary Beth Wood, Managing Editor Judith Grauman,
Senior Book Compositor Oliver Sharpe, and Senior Production Editor Laura Specht Patchkofsky.

acknowledgments

viii 
Acknowledgments

Projects like this always seem to take more time than anticipated, stealing time from other impor-
tant obligations and pursuits. I am grateful for the unflagging support of my wife, Lydia, who gave more
than her share of time to our shared responsibilities when I needed to give more time than expected to
finish the book.

Rick H. Hoyle
Duke University