Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue

by Chow; Sy-Miin

• ISBN13: 9781848728257
• ISBN10: 1848728255
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-12-09
• Publisher: Routledge

Summary

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 at www.psypress.com/chow. 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 datadynamic modeling techniques for intensive repeated measurement datapanel modeling techniques for fewer time points datastate-space modeling techniques for psychological datatechniques used to analyze reaction time dataEach 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.

Table of Contents

Preface	p. xi
Acknowledgments	p. xv
Introduction and Section Overview	p. 1
Part I: Parametric and Exploratory Approaches for Extracting Wimin-Person Nonstationarities	p. 1
Part II: Representing and Extracting Intraindividual Change	p. 4
Part III: Modeling Interindividual Differences in Change and Interpersonal Dynamics	p. 6
References	p. 8
Parametric and Exploratory Approaches for Extracting Within-Person Nonstationarities
Dynamic Modeling and Optimal Control of Intraindividual Variation: A Computational Paradigm for Nonergodic Psychological Processes	p. 13
Introduction	p. 13
Ergodicity	p. 14
(Lack of) Homogeneity	p. 16
Nonstationarity	p. 20
Illustrative EKFIS Application to a Nonstationary Time Series	p. 24
A Monte Carlo Study	p. 27
Optimal Control	p. 31
	p. Conclusion
References	p. 35
Dynamic Spectral Analysis of Biomedical Signals with Application to Electroencephalogram and Heart Rate Variabilityp	p. 39
Introduction	p. 39
Biomedical Signals	p. 41
Time-Frequency Representations	p. 50
Parametric Time-Varying Spectrum Estimation	p. 54
Case Study I: Estimation of ERS of EEG	p. 68
Case Study II: Estimation of HRV Dynamics During an Orthostatic Test	p. 72
Discussion	p. 78
Acknowledgments	p. 80
References	p. 80
Cluster Analysis for Nonstationary Time Series	p. 85
Introduction	p. 85
Fourier Analysis	p. 89
The WP Transform	p. 92
Clustering Nonstationary Time Series	p. 98
Simulations	p. 103
Illustrative Example	p. 109
Summary	p. 112
Acknowledgments	p. 113
Estimation of the Posterior Probability in Equation 4.4	p. 114
BBA for Selecting the Best Clustering Basis	p. 115
Model-Based Feature Selection Algorithm	p. 117
References	p. 120
Chapter 5	p. 123
Introduction	p. 123
Inferring Latent Structure via AR and TVAR Models	p. 127
Detecting Fatigue from EEGs: Experimental Setting and Data Analysis	p. 137
Conclusions and Future Directions	p. 150
Acknowledgments	p. 152
Posterior Estimation in NDLMs	p. 152
References	p. 153
A Closer Look at Two Approaches for Analysis and Classification of Nonstationary Time Series	p. 155
Representing and Extracting Intraindividual Change
Generalized Local Linear Approximation of Derivatives from Time Series	p. 161
Introduction	p. 161
Time Delay Embedding	p. 163
LLA Estimates of Derivatives	p. 165
LDE Estimates of Derivatives	p. 165
Relationship between LLA and the LDE Loading Matrix	p. 167
Simulation	p. 169
Example Application	p. 171
Example Program	p. 173
Modeling Results	p. 174
Discussion	p. 176
Conclusions	p. 176
Acknowledgments	p. 177
References	p. 177
Unbiased, Smoothing-Corrected Estimation of Oscillators in Psychology	p. 179
How do Individuals Change over Time? When? Why?	p. 179
Method for x-Corrected Estimation of Parameters	p. 192
Estimation of co and p	p. 194
Nonoscillating Time Series	p. 201
Conclusions	p. 207
Appendix 8.1	p. 209
References	p. 210
Detrendrng Response Time Series	p. 213
Introduction	p. 213
Motivating Series	p. 217
Defending Methods	p. 220
A Simulation Study	p. 229
Discussion and Conclusions	p. 237
Acknowledgments	p. 238
References	p. 239
Dynamic Factor Analysis with Ordinal Manifest Variables	p. 241
Introduction	p. 241
DFA Models and their Estimation	p. 243
Polychoric Lagged Correlations	p. 246
A Simulation Study	p. 249
An Empirical Example	p. 255
Concluding Comments	p. 260
Acknowledgments	p. 262
References	p. 262
Measuring Intraindividual Variability with Intratask Change Item Response Models	p. 265
Introduction	p. 265
Intratask Change Item Response Models	p. 269
Simulations	p. 275
Example: IIV and Working Memory	p. 277
Discussion	p. 279
Acknowledgments	p. 283
References	p. 283
Modeling Interindividual Differences in Change and Interpersonal Dynamics
Developing a Random Coefficient Model for Nonlinear Repeated Measures Data	p. 289
Introduction	p. 289
Alternative Models for the MNREAD Data	p. 295
A Random Coefficient Model for the MNREAD Data	p. 310
	p. 315
The Quadratic-Linear Model with a Smooth Transition between Phases	p. 316
References	p. 317
Bayesian Discrete Dynamic System by Latent Difference Score Structural Equations Models for Multivariate Repeated Measures Data	p. 319
Be Methods	p. 321
Part I: Fitting a Univariate Latent Difference Score Model	p. 324
Part II: Fitting a Bivariate Difference Score Model	p. 330
Discussion	p. 339
References	p. 345
Longitudinal Mediation Analysis of Training Intervention Effects	p. 349
Introduction	p. 349
Mediation Analysis	p. 350
Methods for the Analysis of Training Intervention with Mediation Effects	p. 353
Empirical Data Analysis	p. 362
Conclusion and Discussion	p. 376
References	p. 378
Exploring Intraindividual, Interindividual, and Intervariable Dynamics in Dyadic Interactions	p. 381
Introduction: Dyadic Interactions	p. 381
Illustrative Data: Daily Fluctuations in Affect	p. 384
Lempell-Ziv (L-Z) Complexity	p. 384
Hierarchical Segmentation	p. 389
Stochastic Transition Networks	p. 398
Discussion	p. 407
Acknowledgment	p. 409
References	p. 409
Author Index	p. 413
Subject Index	p. 421
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