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Data Analytic Techniques for Dynamical Systems

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advanced psychological data analysis
ARMA Structure
Autoregression Matrix
BDI Depression Score
behavioral modeling
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Co-variance Matrix
cognitive neuroscience methods
Covariance Structure Model
cross-lagged panel
developmental processes
Dynamic Modeling Effort
Dynamic Systems Model
Dynamical Systems Analysis
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Full Information Maximum Likelihood
Gestural Responses
Individual Change Trajectory
Latent Change Score Model
Latent Curve Model
Latent Growth Curve
Latent Growth Curve Model
latent variable analysis
LDS
Lower Triangular Toeplitz Matrix
Manifest Variables
nonlinear systems theory
Perceptual Independence
Perceptual Separability
SEM
SEM Framework
SEM Package
SEM Procedure
Simultaneous Equation Models

Product details

  • ISBN 9780805850123
  • Weight: 544g
  • Dimensions: 152 x 229mm
  • Publication Date: 30 Jan 2007
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Each volume in the Notre Dame Series on Quantitative Methodology features leading methodologists and substantive experts who provide instruction on innovative techniques designed to enhance quantitative skills in a substantive area. This latest volume focuses on the methodological issues and analyses pertinent to understanding psychological data from a dynamical system perspective. Dynamical systems analysis (DSA) is increasingly used to demonstrate time-dependent variable change. It is used more and more to analyze a variety of psychological phenomena such as relationships, development and aging, emotional regulation, and perceptual processes.

The book opens with the best occasions for using DSA methods. The final two chapters focus on the application of dynamical systems methods to problems in psychology such as substance use and gestural dynamics. In addition, it reviews how and when to use:

  • time series models from a discrete time perspective

  • stochastic differential equations in continuous time

  • estimating continuous time differential equation models

  • multilevel models of differential equations to estimate within-person dynamics and the corresponding population means

  • new SEM models for dynamical systems data

Data Analytic Techniques for Dynamical Systems is beneficial to advanced students and researchers in the areas of developmental psychology, family studies, language processes, cognitive neuroscience, social and personality psychology, medicine, and emotion. Due to the book’s instructive nature, it serves as an excellent text for advanced courses on this particular technique.
About
Steven M Boker, Michael J. Wenger