Statistical Methods for Modeling Human Dynamics : An Interdisciplinary Dialogue

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.