Table Of ContentModeling Contextual
Effects in Longitudinal
Studies
Modeling Contextual
Effects in Longitudinal
Studies
Edited by
Todd D. Little
University of Kansas
James A. Bovaird
University of Nebraska at Lincoln
Noel A. Card
University of Arizona
Copyright ⃝c 2007, by Lawrence Erlbaum Associates, Inc.
All rights reserved. No part of the book may be reproduced in any form, by
photostat, microform, retrieval system, or any other means, without prior writ-
ten permission of the publisher.
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Cover design by Kathryn Houghtaling
Cover graphics by Elizabeth K. McConnell and Todd D. Little
Library of Congress Cataloging-in-Publication Data
Modeling Contextual Effects in Longitudinal studies
ISBN 978-0-8058-5019-2–ISBN 0-8058-5019-8 (cloth)
ISBN 978-0-8058-6207-2–ISBN 0-8058-6207-2 (pbk)
ISBN 978-1-4106-1587-9–ISBN 1-4106-1587-1 (e book)
Copyright information for this volume can be obtained by contacting the Library of Congress.
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10 9 8 7 6 5 4 3 2 1
Contents
Preface vii
1. Modeling Ecological and Contextual Effects in Longitudinal
Studies of Human Development
Noel A. Card, Todd D. Little and James A. Bovaird 1
2. Statistical Analysis With Incomplete Data:
A Developmental Perspective
Scott M. Hofer and Lesa Hoffman 13
3. Alternatives to Traditional Model Comparison
Strategies for Covariance Structure Models
Kristopher J. Preacher, Li Cai and Robert C. MacCallum 33
4. Impact of Measurement Scale in Modeling Developmental
Processes and Ecological Factors
Susan E. Embretson 63
5. The Incorporation of Categorical Measurement Models
in the Analysis of Individual Growth
Patrick J. Curran, Michael C. Edwards, R. J. Wirth,
Andrea M. Hussong and Laurie Chassin 89
6. Representing Contextual Effects in Multiple-Group MACS Models
Todd D. Little, Noel A. Card, David W. Slegers and
Emily C. Ledford 121
7. Multilevel Structural Equation Models for Contextual Factors
James A. Bovaird 149
8. Mixed-Effects Regression Models With Heterogeneous Variance:
Analyzing Ecological Momentary Assessment (EMA) Data
Donald Hedeker and Robin J. Mermelstein 183
9. Structural Equation Modeling of Mediation and Moderation
With Contextual Factors
Todd D. Little, Noel A. Card, James A. Bovaird,
Kristopher J. Preacher and Christian S. Crandall 207
v
vi CONTENTS
10. Moderating Effects of a Risk Factor: Modeling
Longitudinal Moderated Mediation in the Development of
Adolescent Heavy Drinking
David B. Flora, Siek Toon Khoo and Laurie Chassin 231
11. Modeling Complex Interactions: Person-Centered
and Variable-Centered Approaches
Daniel J. Bauer and Michael J. Shanahan 255
12. Accounting for Statistical Dependency
in Longitudinal Data on Dyads
Niall Bolger and Patrick E. Shrout 285
13. Coupled Dynamics and Mutually Adaptive Context
Steven M. Boker and Jean-Phillipe Laurenceau 299
14. Modeling Intraindividual and Intracontextual Change:
Rendering Developmental Contextualism Operational
Nilam Ram and John R Nesselroade 325
15. The Shape of Things to Come: Diagnosing Social
Contagion From Adolescent Smoking and Drinking Curves
Joseph Lee Rodgers 343
16. A Dynamic Structural Analysis of the Impacts of
Context on Shifts in Lifespan Cognitive Development
Kevin J. Grimm and John J. McArdle 363
17. Intrauterine Environment Affects Infant and Child Outcomes:
Environment as Direct Effect
Keith F. Widaman 387
18. Conceptualizing and Measuring the Context Within Person
←→ Context Models of Human Development: Implications for
Theory, Research and Application
Helena Jelicic, Christina Theokas, Erin Phelps
and Richard M. Lerner 437
Author Index 457
Subject Index 469
Preface
Modeling the impact and influence of contextual factors on human development
is something that many talk about but few actually do. The goal of this book
is to provide researchers with an accessible guide to understanding the many
different ways that contextual factors can be including in longitudinal studies
of human development. In fact, many of the chapters will provide concrete
and clear examples of how contextual factors can be included in most research
studies across the behavioral and social sciences. This book is intended for
researchers in the behavioral social sciences who desire a didactic yet sophisti-
cated treatment of the various ways that contextual factors can be represented
in research studies. As such the content is both accessible and useful for both
graduate students and seasoned investigators.
The introductory chapter of this volume provides a detailed rationale for the
ways in which contextual factors can be represent (e.g., as covariates, predic-
tors, outcomes, moderators, mediators, and mediate effects). The introductory
chapter also serves as the guide to the book so that readers can identify the
needed chapters that will most directly facilitate their needs. The book itself
also covers general issues that are part and parcel of doing best-practice re-
search including such topics as how best to treat missing data in the context of
longitudinal designs (chapter 2), making appropriate model comparisons (chap-
ter 3), and scaling across developmental age ranges (chapter 4). The middle
chapters focus on the use of various statistical techniques from Multilevel mod-
eling (chapter 5 and 8), multiple-group SEM (chapter 6), and multi-level SEM
(chapter 7) as well as how to incorporate complex tests of mediation, mod-
eration and moderated mediation (chapters 9 and 10). The book also covers
person-centered approaches (chapter 11), dyadic and interdependent data de-
signs (chapters 12 and 13), dynamic intra-individual analyses (chapter 14), and
social contagion models (chapter 15). In the later part of the book, critical
vii
viii PREFACE
measurement theoretical issues are discussed including the various ways that
age can be represented (chapter 16) and the ways that context can be concep-
tualized (chapter 17). Finally, the capstone chapter (chapter 18) provides a
succinct and poignant discussion of how all the topics discussed in this book
provide an integrated and compelling call to take context seriously and include
contextual factors in both ones theorizing and ones research.
We wish to thank the ever-present support of Larry Erlbaum, Debra Riegert,
Rebecca Laursen and the many other wonderful folks at LEA who have assisted
us in the process of putting this volume together. Of course, a project such as
this would not be possible without the financial support of key organizations.
Three funding sources provided the financial backing for the conference from
which this volume emerged. Namely, we wish to express our gratitude to the
support of the National Science Foundation (BCS-0345677), the Merrill Ad-
vanced Study Center at the University of Kansas (Mabel Rice, director), and
the Trustees of the Society of Multivariate Experimental Psychology (SMEP).
The editors of this volume would also like to acknowledge key support of other
grants that contributed to the conduct of the work contained herein. Namely,
this work was supported in part by grants from the NIH to the University of
Kansas through the Mental Retardation and Developmental Disabilities Re-
search Center (5 P30 HD002528; Steve Warren, PI), the Center for Biobe-
havioral Neurosciences in Communication Disorders (5 P30 DC005803; Mabel
Rice, PI), an Individual National Research Service Award (F32 MH072005;
Noel Card, PI) and a NFGRF grant (2301779; Todd Little, PI) from the Uni-
versity of Kansas.
—Todd D. Little
Lawrence, Kansas
—James A Bovaird
Lincoln, Nevada
—Noel A Card
Tucson, Arizona
CHAPTER ONE
Modeling Ecological and Contextual Effects
in Longitudinal Studies of Human Development
Noel A. Card
University of Arizona
Todd D. Little
University of Kansas
James A. Bovaird
University of Nebraska at Lincoln
The quality of scientific knowledge is highly dependent on the quality of the
scientific methods used to obtain it. Although it is true that some research
questions are easier to address than others, the steady increases in computa-
tional capabilities over the past decades have spurred a parallel surge in the
complexity of research methodologies and the corresponding data analyses. At
the same time, methodological and analytic techniques do not—and should
not—arise independent of theoretical conceptualization. The coordinated de-
velopment of theory, research methodology, and data analysis is important for
advancing our understanding of human development.
For human development researchers, modeling processes of change, growth,
and transformation represent one of the most difficult, yet necessary, classes
of methodological challenges (see Collins & Sayer, 2001; Little, Schnabel, &
Baumert, 2000). Not only are longitudinal studies our best alternative in many
situations for attempting to infer directions of causal influence, but the study
of change over time is of itself a central consideration in studying human de-
velopment. Various analytic approaches have been used to study this change.
Basic regression analyses, and better yet, latent structural models, have been
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