Statistical Methods for Modeling Human Dynamics

An Interdisciplinary Dialogue

By: Sy-Miin Chow (Editor), Emilio Ferrer (Editor), Fushing Hsieh (Editor)

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Hardcover | 9 December 2009 | Edition Number 1

At a Glance

Hardcover
446 Pages

Dimensions(cm)
22.86 x 15.24 x 2.54

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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:

Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience data
Dynamic modeling techniques for intensive repeated measurement data
Panel modeling techniques for fewer time points data
State-space modeling techniques for psychological data
Techniques used to analyze reaction time data.

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.

Industry Reviews

"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

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