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Statistical Methods for Modeling Human Dynamics

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Active Study
analysis
AR Model
Bayesian structural models
Category=JMB
Category=JMM
Category=JMR
Category=PSAN
Data Set
DFA Model
Differential Equation Modeling
dyadic interaction modeling
EEG data analysis
EEG Signal
embedded
Embedding Dimension
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
eq_society-politics
Everyday Problem Test
factor
filter
hierarchical time series
intraindividual variability
kalman
Latent Difference Score
lds
LDS Model
lengths
Linear Linear Model
Log Mar
Log Rt
Longitudinal Mediation Analysis
Longitudinal Mediation Model
Measurement Occasion
models
nonlinear psychological process modeling
Nonstationary Time Series
PANAS Item
Phase Resetting
Polychoric Correlation
R statistical programming
Random Coefficient Model
series
time
Time Series Lengths
Wavelet Packet
WP Coefficient

Product details

  • ISBN 9781848728257
  • Weight: 980g
  • Dimensions: 152 x 229mm
  • Publication Date: 09 Dec 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About
Sy-Miin Chow is Assistant Professor of Psychology at the University of North Carolina at Chapel Hill. She received her Ph.D. in Quantitative Psychology from the University of Virginia. Her research focuses on the development and adaptation of modeling and analysis tools for evaluating linear and nonlinear dynamical systems models. Dr. Chow received the prestigious Dissertation Award from the Society of Multivariate Experimental Psychology in 2004. Emilio Ferrer is Associate Professor of Psychology at the University of California, Davis. He received his Ph.D. in Quantitative Psychology from the University of Virginia. His research focuses on methods techniques for studying change and intra-individual variability in developmental processes. Dr. Ferrer received the prestigious Dissertation Award from the Society of Multivariate Experimental Psychology in 2002. Fushing Hsieh is Professor of Statistics at the University of California, Davis. He received his Ph.D. in Statistics from  Cornell University. Dr. Hsieh's research focuses on survival analysis, modeling in biomedical dynamic systems and in animal behavior, evolutionary ecology and aging, and the analysis of cognitive processing. A frequent contributor to Biometrika and the Journal of the Royal Statistical Society Series B, Dr. Hsieh served as an Associate Editor of Statistica Sinica from 1998 until 2005.