Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data

Stephen J. Guastello, Robert A.M. Gregson

November 17, 2010 by CRC Press
Reference - 631 Pages - 188 B/W Illustrations
ISBN 9781439819975 - CAT# K11053


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  • Provides information centered on statistical theory that accompanies NDS constructs
  • Explores techniques that have proven to be most useful in the behavioral sciences
  • Takes a how-to approach featuring instructions for the most accessible software
  • Complies the most relevant research methods for nonlinear dynamical systems (NDS) psychology and closely related disciplines
  • Conveys theoretical and practical knowledge of the techniques and ways of framing research problems and designs to use them properly
  • Addresses topics and methods of current interest in an application driven manner


Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflecting the expertise of major contributors to NDS psychology, Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data examines the techniques proven to be the most useful in the behavioral sciences.

The editors have brought together constructive work on new practical examples of methods and application built on nonlinear dynamics. They cover dynamics such as attractors, bifurcations, chaos, fractals, catastrophes, self-organization, and related issues in time series analysis, stationarity, modeling and hypothesis testing, probability, and experimental design. The analytic techniques discussed include several variants of the fractal dimension, several types of entropy, phase-space and state-space diagrams, recurrence analysis, spatial fractal analysis, oscillation functions, polynomial and Marquardt nonlinear regression, Markov chains, and symbolic dynamics.

The book outlines the analytic requirements faced by social scientists and how they differ from those of mathematicians and natural scientists. It includes chapters centered on theory and procedural explanations for running the analyses with pertinent examples and others that illustrate applications where a particular form of analysis is seen in the context of a research problem. This combination of approaches conveys theoretical and practical knowledge that helps you develop skill and expertise in framing hypotheses dynamically and building viable analytic models to test them.