Models for Intensive Longitudinal Data

by Walls, Theodore A.; Schafer, Joseph L.

• ISBN13: 9780195173444
• ISBN10: 0195173449
• Format: Hardcover
• Copyright: 2006-01-19
• Publisher: Oxford University Press

Summary

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 forcollecting 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 statisticalmodeling 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 kindsof 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 principalinvestigators 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.

Author Biography

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.

Table of Contents

Contributors	ix
Introduction: Intensive Longitudinal Data	xi
Multilevel Models for Intensive Longitudinal Data	3	(35)
Theodore A. Walls
Hyekyung Jung
Joseph E. Schwartz
Behavioral Scientific Motivations for Collecting Intensive Longitudinal Data	3	(2)
Overview of Multilevel Models	5	(8)
Applying Multilevel Modeling to Intensive Longitudinal Data	13	(14)
Application: Control and Choice in Indian Schoolchildren	27	(6)
Summary	33	(5)
Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations	38	(25)
Joseph L. Schafer
What Is GEE Regression?	39	(9)
Practical Considerations in the Application of GEE	48	(7)
Application: Reanalysis of the Control and Choice Data Using GEE	55	(8)
A Local Linear Estimation Procedure for Functional Multilevel Modeling	63	(21)
Runze Li
Tammy L. Root
Saul Shiffman
The Model	65	(6)
Practical Considerations	71	(1)
Application: Smoking Cessation Study	72	(8)
Discussion	80	(4)
Application of Item Response Theory Models for Intensive Longitudinal Data	84	(25)
Donald Hedeker
Robin J. Mermelstein
Brian R. Flay
IRT Model	85	(7)
Estimation	92	(2)
Application: Adolescent Smoking Study	94	(9)
Discussion	103	(6)
Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data	109	(15)
Carlotta Ching Ting Fok
James O. Ramsay
Periodic and Nonperiodic Trends	109	(3)
The Model	112	(8)
Application: Personality Data	120	(2)
Discussion	122	(2)
Multilevel Autoregressive Modeling of Interindividual Differences in the Stability of a Process	124	(24)
Michael J. Rovine
Theodore A. Walls
Defining Stability as Regularity in a Time Series	125	(1)
Multilevel Models	126	(5)
A Multilevel AR(1) Model	131	(2)
Application: Daily Alcohol Use	133	(1)
Estimating This Model in SAS Proc Mixed	134	(4)
Predicting the Individual AR(1) Coefficients	138	(5)
Discussion	143	(5)
The State-Space Approach to Modeling Dynamic Processes	148	(28)
Moon-Ho Ringo Ho
Robert Shumway
Hernando Ombao
Gaussian State-Space Models	149	(3)
Some Special Cases of State-Space Models	152	(4)
Parameter Estimation	156	(3)
Application 1: Connectivity Analysis with fMRI Data	159	(6)
Application 2: Testing the Induced Demand Hypothesis from Matched Traffic Profiles	165	(5)
Conclusions	170	(6)
The Control of Behavioral Input/Output Systems	176	(19)
James O. Ramsay
A Typical Input/Output System	177	(2)
Modeling System Dynamics	179	(4)
Controller Strategies to Meet an Output Target	183	(6)
Fitting Dynamic Models to Intensive Longitudinal Data	189	(6)
Dynamical Systems Modeling: An Application to the Regulation of Intimacy and Disclosure in Marriage	195	(24)
Steven M. Boker
Jean-Philippe Laurenceau
Self-Regulation and Intrinsic Dynamics	195	(5)
Coupled Regulation and Coupled Dynamics	200	(3)
Time-Delay Embedding	203	(2)
Accounting for Individual Differences in Dynamics	205	(1)
Application: Daily Intimacy and Disclosure in Married Couples	206	(9)
Discussion	215	(4)
Point Process Models for Event History Data: Applications in Behavioral Science	219	(35)
Stephen L. Rathbun
Saul Shiffman
Chad J. Gwaltney
Ecological Momentary Assessment of Smoking	222	(2)
Point Process Models	224	(4)
Application: An EMA Study of Smoking Data	228	(14)
Discussion of Results	242	(3)
Multivariate Point Patterns	245	(9)
Emerging Technologies and Next-Generation Intensive Longitudinal Data Collection	254	(25)
Sarah M. Nusser
Stephen S. Intille
Ranjan Maitra
Intensive Data Collection Systems	256	(9)
Statistical Issues for Intensive Longitudinal Measurement	265	(9)
Summary	274	(5)
Index	279