Models for Intensive Longitudinal Data

Front Cover
Theodore A. Walls, Joseph L. Schafer
Oxford University Press, Jan 19, 2006 - Mathematics - 320 pages
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.
 

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Contents

1 Multilevel Models for Intensive Longitudinal Data
3
2 Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations
38
3 A Local Linear Estimation Procedure for Functional Multilevel Modeling
63
4 Application of Item Response Theory Models for Intensive Longitudinal Data
84
5 Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data
109
6 Multilevel Autoregressive Modeling of Interindividual Differences in the Stability of a Process
124
7 The StateSpace Approach to Modeling Dynamic Processes
148
8 The Control of Behavioral InputOutput Systems
176
An Application to the Regulation of Intimacy and Disclosure in Marriage
195
Applications in Behavioral Science
219
11 Emerging Technologies and NextGeneration Intensive Longitudinal Data Collection
254
Index
279
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About the author (2006)

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.

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