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Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data

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BDS Statistic
Bds Statistics
BDS Test
behavioral data analysis
Catastrophe Model
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chaos theory applications
Chaotic Attractor
Cusp Catastrophe
Cusp Model
Detrended Fluctuation Analysis
Embedding Dimensions
entropy measurement techniques
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experimental design psychology
FI Schedule
fractal analysis methods
Fractal Dimension
Interresponse Time
Inverse Power Law Distribution
LLE
Markov
Multiple Linear Regression
Nds
Nonlinear Dynamical
Nonlinear Dynamical Analyses
nonlinear dynamics research guide
Nonlinear Time Series Analysis
Orbital Decomposition
Oscillators
phase space visualization
Recurrence Plots
Self-organized Criticality
Short RSI
SRM
State Space Grids
Topological Entropy
Transition Matrix

Product details

  • ISBN 9781439819975
  • Weight: 1330g
  • Dimensions: 156 x 234mm
  • Publication Date: 17 Nov 2010
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

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.
About

Stephen Gaustello is a professor of Psychology at Marquette University, in Milwaukee Wisconsin.