Ebook: Data Analytic Techniques for Dynamical Systems
Author: Steven M Boker Michael J. Wenger
- Genre: Psychology
- Series: Notre Dame Series on Quantitative Methodology
- Year: 2007
- Publisher: Psychology Press
- Edition: 1.
- Language: English
- pdf
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.
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.
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