This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA.
Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of:
Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.
"This is a timely and important book that addresses an integrated set of topics that will be of great interest to a broad audience of researchers studying human dynamics. The contributors are among the leaders in their respective fields and jointly represent a truly interdisciplinary perspective on these issues. The material is presented in both an accessible and technically rigorous manner, and real data examples help clarify key points throughout. Importantly, this text offers a collection of papers that challenge many traditional beliefs held about the "typical" analysis of repeated measures data. I recommend this book highly." - Patrick J. Curran, University of North Carolina, Chapel Hill, USA
"Dynamical modeling of intraindividual change and variability obtained from many sources and in a wide range of time scales is the promising future of behavioral science research and the availability of this outstanding volume will accelerate our progress in that direction. Take it home, take it to the office, take it to class and, whatever you do, take it seriously!" - John R. Nesselroade, University of Virginia, USA
"Intensive longitudinal designs will be providing cutting edge insights into psychological, neuroscience and biomedical processes during the next decades, and researchers using these designs will want to study the useful approaches described in this volume. I consider this book to be required reading!" - Patrick E. Shrout, New York University, USA
"I am delighted to see this outstanding volume containing contributions connecting mathematics, classical and Bayesian statistics, signal processing and psychology. .. Other subject matter areas could well take this volume as a model for presenting the results of collaborative research in their own fields."-Robert H. Shumway, University of California, Davis, USA
Introduction and Section Overview. Part 1. Parametric and Exploratory Approaches for Extracting Within-Person Nonstationarities. P.C.M. Molenaar, N. Ram, Dynamic Modeling and Optimal Control of Intra-Individual Variation: A Computational Paradigm for Non-Ergodic Psychological Processes. M. Tarvainen, Dynamic Spectral Analysis of Biomedical Signals with Application to EEG and Heart Rate Variability. B. Gao, H. Ombao, M.R. Ho, Cluster Analysis for Non-Stationary Time Series. R. Prado, Characterizing Latent Structure in Brain Signals. H. Ombao, R. Prado, A Closer Look at Two Approaches for Analysis and Classification of Non-Stationary Time Series. Part 2. Representing and Extracting Intraindividual Change. S. Boker, P.R. Deboeck, C. Edler, P. Keep, Generalized Local Linear Approximation of Derivatives from Time Series. P.R. Doebeck, S.M. Boker, Unbiased, Smoothing-Corrected Estimation of Oscillators in Psychology. P.F. Craigmile, M. Peruggia, T. Van Zandt, Detrending Response Times Series. G. Zhang, M.W. Browne, Dynamic Factor Analysis with Ordinal Manifest Variables. R.P. Bowles, Measuring Intraindividual Variability with Intratask Change Using Item Response Models. Part 3. Modeling Interindividual Differences in Chang and Interpersonal Dynamics. R. Cudeck, J. Harring, Developing a Random Coefficient Model for Nonlinear Repeated Measures Data. F. Hamagami, Z.J. Zhang, J. McArdle, A Bayesian Discrete Dynamic System by Latent Difference Score Structural Equations Models for Multivariate Repeated Measures Data. L. Wang, Z. Zhang, R. Estabrook, Longitudinal Mediation Analysis of Training Intervention Effects. F. Hsieh, S. Chen, S. Chow, E. Ferrer, Exploring Intra-Individual, Inter-Individual and Inter-Variable Dynamics in Dyadic Interactions.
The Notre Dame Series on Quantitative Methodology offers advanced training in quantitative methods for social and behavioral research.
Leading experts in data analytic techniques provide instruction in state-of-the-art methods designed to enhance quantitative skills in a substantive domain.
Each volume brings together expert methodologists and a workshop audience of substantive researchers. The substantive researchers are challenged with innovative techniques and the methodologists are challenged by innovative applications.
The goal is to stimulate an emergent substantive and methodological synthesis, enabling the solution of existing problems and raising new questions that need to be asked.
Each volume targets researchers in a specific substantive area, but also contains innovative techniques of interest to pure methodologists.