Modeling contextual effects in longitudinal studies:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Psychology Pr.
2009
|
| Ausgabe: | 1. publ. by Erlbaum, repr. |
| Schlagworte: | |
| Online-Zugang: | Publisher description Contributor biographical information Table of contents only Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and indexes |
| Beschreibung: | VIII, 471 S. Ill., graph. Darst. |
| ISBN: | 0805850198 9780805850192 0805862072 9780805862072 9781410615879 1410615871 |
Internformat
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| 245 | 1 | 0 | |a Modeling contextual effects in longitudinal studies |c ed. by Todd D. Little ... |
| 250 | |a 1. publ. by Erlbaum, repr. | ||
| 264 | 1 | |a New York [u.a.] |b Psychology Pr. |c 2009 | |
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Datensatz im Suchindex
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| adam_text | 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. Siegers 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
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
Nilarn Ram and John
R Nesselroade
325
15.
The Shape of Things to Come: Diagnosing Social
Contagion Prom 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
Jelicie,
Christina Theokas, Erin Phelpe
and Richard M.
Lerner 437
Author Index
457
Subject Index
469
Modeling
Contextual Effects
in Longitudinal Studies
Longitudinal data are critical for understanding how individuals change across time.
Researchers are faced with a complex task when modeling the contexts in which
longitudinal processes unfold. Modeling Contextual Effects in Longitudinal Studies reviews
the challenges and alternative approaches to modeling these influences and provides
methodologies and data analytic strategies for behavioral and social science researchers,
This accessible guide provides concrete, clear examples of how contextual factors can be
included in most research studies. Each chapter can be understood independently, allowing
readers to first focus on areas most relevant to their work. The opening chapter demonstrates
the various ways contextual factors are represented
—
as covariates, predictors, outcomes,
moderators, mediators, or mediated effects. Succeeding chapters review best practice
techniques for treating missing data, making model comparisons, and scaling across
developmental age ranges. Other chapters focus on specific statistical techniques such as
multilevel modeling and multiple-group and multilevel
SEM,
and how to incorporate tests of
mediation, moderation, and moderated mediation.
Criticai
measurement and theoretical
issues are discussed, particularly how age can be represented and the ways in which
context can be conceptualized. The final chapter provides a compelling call to include
contextual factors in theorizing and research.
Modeling Contextual Effect in Longitudinal Studies will appeal to researchers and advanced
students conducting developmental, social, clinical, or educational research, as well as those
in related areas such a psychology and linguistics.
mm About the Editors
Todd, D. Little is Director of the Research, Design
&
Analysis Unit
ano!
the Quantitative
Psychology Doctoral Training Program and a Professor of Psychology at the University of
Kansas. He received his PhD in developmental and quantitative psychology at the University
of California. Riverside. Dr. Little has extensive experience in the use of longitudinal research
methods and he has edited several LEA books on the subject.
James A. Bovaird is an Assistant Professor in Educational Psychology at the University of
Nebraska
—
Lincoln. He received his PhD in quantitative psychology at the University of
Kansas. His quantitative interests are in the application of latent variable methodologies to
novel substantive areas and the evaluation of these methodologies in situations of limited
inference.
Nos! A,
Card is an Assistant Professor in
the Division of
Farntîy
Studies and
Human Development at the University of
Arizona. He received his PhD in clinical
psychology from St. Johns University.
His quantitative interests are
structurai
equation modeling,
tongitudinaì design
and analysis, meta-analysls. and analyz¬
ing interdependent data.
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| spellingShingle | Modeling contextual effects in longitudinal studies Mathematisches Modell (DE-588)4114528-8 gnd Längsschnittuntersuchung (DE-588)4034036-3 gnd Psychologie (DE-588)4047704-6 gnd Soziologie (DE-588)4077624-4 gnd |
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| title | Modeling contextual effects in longitudinal studies |
| title_auth | Modeling contextual effects in longitudinal studies |
| title_exact_search | Modeling contextual effects in longitudinal studies |
| title_full | Modeling contextual effects in longitudinal studies ed. by Todd D. Little ... |
| title_fullStr | Modeling contextual effects in longitudinal studies ed. by Todd D. Little ... |
| title_full_unstemmed | Modeling contextual effects in longitudinal studies ed. by Todd D. Little ... |
| title_short | Modeling contextual effects in longitudinal studies |
| title_sort | modeling contextual effects in longitudinal studies |
| topic | Mathematisches Modell (DE-588)4114528-8 gnd Längsschnittuntersuchung (DE-588)4034036-3 gnd Psychologie (DE-588)4047704-6 gnd Soziologie (DE-588)4077624-4 gnd |
| topic_facet | Mathematisches Modell Längsschnittuntersuchung Psychologie Soziologie |
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