Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for
collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical
modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use,
traffic patterns, educational performance and intimacy.
Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of
data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal
investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation.
Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for
collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical
modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use,
traffic patterns, educational performance and intimacy.
Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of
data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal
investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation.
Introduction: Intensive Longitudinal DataTheodore A. Walls and
Joseph L. Schafer:
1: Theodore A. Walls, Hyekyung Jung, and Joseph E. Schwartz:
Multilevel Models for Intensive Longitudinal Data
2: Joseph L. Schafer: Marginal Modeling of Intensive Longitudinal
Data by Generalized Estimating Equations
3: Runze Li, Tammy L. Root, and Saul Shiffman: A Local Linear
Estimation Procedure for Functional Multilevel Modeling
4: Donald Hedeker, Robin J. Mermelstein, and Brian R. Flay:
Application of Item Response Theory Models for Intensive
Longitudinal Data
5: Carlotta Ching Ting Fok and James O. Ramsay: Periodic Trends,
Non-periodic Trends, and their Interactions in Longitudinal or
Functional Data
6: Michael J. Rovine and Theodore A. Walls: Multilevel
Autoregressive Modeling of Interindividual Differences in the
Regularity of a Process
7: Moon-Ho Ringo Ho, Robert Shumway, and Hernando Ombao: The
State-Space Approach to Modeling Dynamic Processes
8: James O. Ramsay: The Control of Behavioral Input/Output
Systems
9: Steven M. Boker and Jean-Phillippe Laurenceau: Dynamical Systems
Modeling: An Application to the Regulation of Intimacy and
Disclosure in Marriage
10: Stephen L. Rathbun, Saul Shiffman, and Chad J. Gwaltney: Point
Process Models for Event History Data: Applications ion the
Behavioral Science
11: Sarah M. Nusser, Stephen S. Intille, and Ranjan Maitra:
Emerging Technologies and Next Generation Intensive Longitudinal
Data Collection
Theodore A. Walls, Ph.D., is Professor of Psychology at the
University of Rhode Island. As a research scientist at The
Methodology Center at The Pennsylvania State University, Dr. Walls
developed methods for the analysis of intensive longitudinal data
and convened the international study group whose work led to the
publication of this volume. His current work is focused on the
development of models reflecting dynamic intraindividual
processes.
Joseph L. Schafer, Ph.D., is Associate Professor of Statistics and
an Investigator at The Methodology Center at The Pennsylvania State
University. Dr. Schafer has developed techniques for analyzing
incomplete data and incorporating missing-data uncertainty into
statistical inference. His areas of research also include
latent-class and latent transition analysis, nonsampling errors in
surveys and censuses, strategies for statistical computing and
software development, and
statistical methods for casual inference.
"The topics covered and the multidisciplinary authors make it
appealing to a very wide range of researchers in statistics and the
social and behavioral sciences."-- Technometrics
"Walls and Schafer have compiled a most interesting and practical
volume on methods of analysis of what they call Intensive
Longitudinal Data--data from more than just three or four
observation waves. This book is interesting because it shows that
new and unusual hypotheses can be addressed to complex data, and
practical because the methods discussed and proposed are applicable
and programs will run on regular PCs. The topics addressed and
the
multidisciplinary authors make this volume appealing to a very wide
readership in biostatistics and the social and behavioral sciences.
This is a groundbreaking book for the emerging field of statistical
modeling of
intensive longitudinal data!"--Alexander von Eye, Professor of
Psychology, Michigan State University
"From Palm Pilots to wearable computers to GPS monitors, modern
technology is allowing today's empirical researchers to generate
vast quantities of longitudinal data quickly and easily. But how
should these data be analyzed? Models for Intensive Longitudinal
Data provides a wonderful overview of the wide array of new
analytic options...a hitchhiker's guide to an exciting new galaxy
in longitudinal data analysis."--Judith D. Singer, James Bryant
Conant Professor of Education, Harvard Graduate School of
Education.
"Intensive longitudinal data is a fascinating and burgeoning field
of statistical endeavor. MILD is a spicy introduction to a colorful
subject."--Robert E. Weiss, Professor of Biostatistics, UCLA School
of Public Health
"Clearly, the doors to an exciting line of research and application
have been opened. The editors did an excellent job, making sure the
number of errors is minimal, the level of exposition is comparable
over the chapters, and chapters cross-reference each other where
meaningful. It is not very often that researchers detect a niche
and go successfully about establishing and filling it. Walls and
Schafer have accomplished this task."--Alexander von Eye,
Structural Equation Modeling: A Multidisciplinary Journal
"Although this volume will be of greatest value to quantitatively
oriented researchers in psychology and other areas of social
science, I also recommend it to the statisticians and anyone else
interested in the collection and analysis of ILD.--Journal of the
American Statistical Association
"I recommend it highly and hazard the opinion that sociologists,
demographers, and related social scientists will develop their own
companions to this volume in future years."--American Journal of
Sociology
"[Models for Intensive Longitudinal Data] addresses most of the
researchers in the behavioral and related sciences, such as
psychology, sociology, education, economics, management, and
medical sciences. The book also addresses methodologists and
statisticians, who are professionally dealing with longitudinal
analysis, to enhance their knowledge of the type of model covered
and the technical problems involved in their formulation. In
addition, the book
offers applied researchers new ideas about the use of longitudinal
analysis in solving their problems."--Psychometrika
"The topics covered and the multidisciplinary authors make it
appealing to a very wide range of researchers in statistics and the
social and behavioral sciences."-- Technometrics
"Walls and Schafer have compiled a most interesting and practical
volume on methods of analysis of what they call intensive
longitudinal data--data from more than just three or four
observation waves. The book is interesting because it shows that
new and unusual hypotheses can be addressed to complex data, and
practical because the methods discussed and proposed are applicable
and programs will run on regular PCs. The topics addressed and
the
multidisciplinary authors make this volume appealing to a very wide
readership in biostatistics and the social and behavioral sciences.
This is a groundbreaking book for the emerging field of statistical
modeling of
intensive longitudinal data!"--Alexander von Eye, Professor of
Psychology, Michigan State University
"From Palm Pilots to wearable computers to GPS monitors, modern
technology is allowing today's empirical researchers to generate
vast quantities of longitudinal data quickly and easily. But how
should these data be analyzed? Models for Intensive Longitudinal
Data provides a wonderful overview of the wide array of new
analytic options...a hitchhiker's guide to an exciting new galaxy
in longitudinal data analysis."--Judith D. Singer, James Bryant
Conant Professor of Education, Harvard Graduate School of
Education.
"Intensive longitudinal data is a fascinating and burgeoning field
of statistical endeavor. MILD is a spicy introduction to a colorful
subject."--Robert E. Weiss, Professor of Biostatistics, UCLA School
of Public Health
"Clearly, the doors to an exciting line of research and application
have been opened. The editors did an excellent job, making sure the
number of errors is minimal, the level of exposition is comparable
over the chapters, and chapters cross-reference each other where
meaningful. It is not very often that researchers detect a niche
and go successfully about establishing and filling it. Walls and
Schafer have accomplished this task."--Alexander von Eye,
Structural Equation Modeling: A Multidisciplinary Journal
"I recommend it highly and hazard the opinion that sociologists,
demographers, and related social scientists will develop their own
companions to this volume in future years."--American Journal of
Sociology
"[Models for Intensive Longitudinal Data] addresses most of the
researchers in the behavioral and related sciences, such as
psychology, sociology, education, economics, management, and
medical sciences. The book also addresses methodologists and
statisticians, who are professionally dealing with longitudinal
analysis, to enhance their knowledge of the type of model covered
and the technical problems involved in their formulation. In
addition, the book
offers applied researchers new ideas about the use of longitudinal
analysis in solving their problems."--Psychometrika
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