Handbook of Developmental Research Methods

801 Pages·2011·12.917 MB·English

by  Brett Laursen, Todd D. Little, Noel A. Card

H a n d b o o k o f
d e v e lo p m e n ta l
R es e a R c H m e tH o d s

H
andbook of
d
evelopmental
R m
eseaRcH etHods
Edited by
BrEtt LaursEn
todd d. LittLE
noEL a . Card
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 developmental research methods / edited by Brett Laursen,
Todd D. Little, Noel A. Card.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-60623-609-3 (hardcover)
1. Developmental psychology—Research—Methodology. 2. Developmental
psychology—Longitudinal studies. I. Laursen, Brett Paul. II. Little, Todd D.
III. Card, Noel A.
BF713.H3643 2012
155.072′1—dc23
2011019742
preface
M
any handbooks claim to be essential. This one truly is. If you study development, it is
unlikely that you have been able to keep up with the rapid changes in analytic methodolo-
gies. Even if your job title includes “advanced methodologist,” it is unlikely that you have
been able to stay abreast of the latest offerings from your colleagues.
There are plenty of handbooks on research methods, but none are aimed specifically
at developmental scholars. This is a difference with a distinction. To study development
is to study change. Each of the chapters in this volume is focused on applying a method-
ological tool to the measurement of change. Some tools were specifically designed to mea-
sure change. Other tools have been adapted to incorporate the demands of developmental
research. Developmental scholars no longer have to make do with figuring out how static
approaches can be applied to dynamic data, because the world’s leading developmental
methodologists have applied themselves to this task.
The results are pleasantly overwhelming. Not long ago, developmental scientists needed
only to be conversant in repeated-measures analyses of variance and stepwise regression.
There are now dozens of alternatives to these techniques, most of which are more flexible
and more powerful. Unlike their predecessors, these new techniques continue to evolve and
improve. Powerful new software drives many of these advances. But keeping up with the
pace of change in developmental research methods requires more than software upgrades.
One must understand the fundamentals at the heart of each method in order to understand
how change is measured and represented.
Lars Bergman, the wise Swedish methodologist, often reminds us to examine the
assumptions of an analytic procedure. One need not be able to conduct complicated analyses
in order to understand them. Unfortunately, many statistical packages make the analyses
easy and the assumptions opaque to the point that it can sometimes be difficult to exam-
ine the assumptions. Each contributor to this handbook was tasked with the challenge of
explaining the assumptions of a developmental method in accessible nontechnical language.
The reader should come away from each chapter understanding the basic purpose of a
procedure and the principles that animate that procedure. If we have been successful, the
relevance of each method to your own research should be apparent.
v
vi Preface
The volume is more than a primer, however. Methodologists will be pleased to hear
that expositions of assumptions are followed by a careful explication of recent technical
advances. This is not an abstract exercise. In most cases, the summary of cutting-edge
practices is accompanied by a specific research example that concretizes the procedure.
These illustrations should prove useful to readers hoping to apply the technique to their
own data. Many authors have helpfully provided scripts and data samples to facilitate the
application process. These supplemental materials are available online at www.crmda.
ku.edu/Guilford/HDRM.
The volume is divided into seven sections. The opening section on measurement and
design is followed by a section on approaches to data collection. These chapters are devoted
to issues that concern the design and implementation of developmental research. The next
three sections concern interindividual longitudinal analysis, intraindividual longitudinal
analysis, and analyses that combine the two. These chapters describe cutting-edge analytic
practices designed specifically for developmental data. The section that follows addresses
the burgeoning topic of nonindependent data with a unique collection of chapters devoted
to developmental applications. The volume closes with a section on special topics in data
analysis, a heading that encompasses important research methods that tend to be underuti-
lized by developmental scholars. All but the last section open with a unique chapter on foun-
dational issues written by eminent developmental methodologists. We gave these scholars
free rein in defining the material covered, instructing them only to provide an expansive
overview of the field. Except for those addressing foundational issues, the chapters in the
handbook follow a similar outline: (1) central issues; (2) conceptual principles and statistical
assumptions; (3) developmental applications; (4) illustration; and (5) future directions.
In keeping with the admonition of our esteemed colleague, let us be specific about our
assumptions. We consider change to be central to the study of development. Contemporary
students might be amazed to learn that within their lifetimes, cross-sectional data were
used to gauge developmental trends and the presence of statistically significant differences
in one age group and that the absence of such differences in another was thought to consti-
tute a developmental finding of note. This sort of methodology is no longer tenable; develop-
ment cannot be measured in the absence of data from multiple time points. The chapters
in this volume take as their starting point the assumption that development is synonymous
with change and that longitudinal data are a prerequisite for the study of change.
Change can be measured at the level of the variable or at the level of the individual.
Longitudinal change can be examined between and within individuals using interindividual
and intraindividual procedures, respectively. The assumptions of each are quite different.
Studies that demonstrate change at the level of the variable assume that the developmental
trends illustrated apply equally well to all members of the sample or population. Studies that
demonstrate change at the level of the individual assume that the unique developmental
patterns shared across subgroups of individuals do not apply to the population as a whole.
Some have described these approaches as antithetical, but we prefer to think of them as
complementary. Both approaches are well represented in this book.
We assume the high standard of best methodological practices. Scholars have long
known of biases arising from samples of convenience, statistical nonindependence, demand
characteristics, recall load, attrition, and measurement and scaling variance. There was once
a good excuse for sweeping these problems aside, namely, the absence of a remedy. This is
no longer the case, and those who ignore these biases do so at their own peril. This hand-
book contains several chapters that identify and explain sources of bias in developmental
Preface vii
research. Importantly, each chapter also describes methods for assessing and addressing
the challenges that arise in developmental research. Forewarned is forearmed: Many meth-
odological problems can be avoided or mitigated with appropriate design strategies. Look
for tips on strengthening developmental research with designs that anticipate potential
sources of bias.
Finally, we assume that research methods are tools designed to aid the scholar in iden-
tifying developmental change. Important implications follow from this assumption. Different
tools can be selected for the same job. Holes can be made with hammers, and holes can be
made with drills. The choice of the appropriate tool depends on the aims of the investigator.
Some tools are clearly inappropriate for the task at hand. One cannot install a window with
tools designed to dig ditches. Thus research questions must be matched to developmental
methods. We strongly believe that an investigator cannot make an informed choice without
a working knowledge of how each research method works. Research questions often carry
hidden assumptions. Developmental questions are informed by the models and ontogenetic
theories of the investigator. Hypotheses usually call for certain types of methodologies.
Because the assumptions of methodological tools differ, similar data or even the same data
may yield different depictions of change. Put another way, research questions drive the
selection of developmental methods, which can foreshadow whether and how developmen-
tal changes are described. The chapters in this handbook will help to clarify the unappreci-
ated link between research questions, methods, and developmental change by illuminating
the assumptions inherent in each.
A volume of this magnitude requires enormous effort from many parties. Todd Little
and Noel Card are as knowledgeable as they are congenial; I could not ask for better col-
laborators. Shalynn Howard cheerfully compiled and organized the chapters. Jeff Friedrich
assisted with proofreading. The Center for Research Methods and Data Analysis at the Uni-
versity of Kansas (Todd D. Little, Director) graciously agreed to host and maintain the web-
site dedicated to the ancillary materials from this volume at wwwcrmda.ku.edu/Guilford/
HDRM. C. Deborah Laughton and Seymour Weingarten of The Guilford Press convinced
us of the merits of this project and worked to meet what must have seemed like unreason-
able demands. Thanks to our families (Erika, Kirsten, and Erik; Patty; Jeanet, Gabby, and
Angie), whose love and forbearance were tested but never found lacking. We are gratified by
the cooperation we received from our methodological colleagues; most of those who were
invited to participate agreed to do so. Our contributors were, for the most part, a timely and
responsive bunch. Their collective wisdom is humbling.
Brett Laursen
Fort Lauderdale, Florida
January 2011

contents
paRt I. Measurement and Design
Chapter 1. FoundationaL issuEs of design and Measurement 3
in developmental research
Scott M. Hofer, Valgeir Thorvaldsson, and Andrea M. Piccinin
Chapter 2. Causal inference, identification, and Plausibility 17
E. Michael Foster
Chapter 3. accelerated Longitudinal designs 31
Susan C. Duncan and Terry E. Duncan
Chapter 4. time-scale- dependent Longitudinal designs 46
Theodore A. Walls, William D. Barta, Robert S. Stawski, Charles E. Collyer,
and Scott M. Hofer
Chapter 5. Event Frequency Measurement 65
Brett Laursen, Jaap Denissen, and David F. Bjorklund
Chapter 6. the impact of scaling and Measurement Methods 82
on individual differences in Growth
Susan E. Embretson and John Poggio
Chapter 7. investigating Factorial invariance in Longitudinal data 109
Roger E. Millsap and Heining Cham
paRt II. approaches to Data Collection
Chapter 8. FoundationaL issuEs in Longitudinal data Collection 129
Lea Pulkkinen and Katja Kokko
Chapter 9. the use of Large-scale data sets for the study 148
of developmental science
Pamela Davis-Kean and Justin Jager
Chapter 10. telemetrics and online data Collection: Collecting data 163
at a distance
Joshua Wilt, David M. Condon, and William Revelle
ix