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E-raamat: Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue

Edited by (University of North Carolina - Chapel Hill, USA), Edited by (University of California, USA), Edited by (University of California, Davis, USA)
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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.

Arvustused

"This is a timely and important book that addresses an integrated set of topics that will be of great interest to a broad audience of researchers studying human dynamics. The contributors are among the leaders in their respective fields and jointly represent a truly interdisciplinary perspective on these issues. The material is presented in both an accessible and technically rigorous manner, and real data examples help clarify key points throughout. Importantly, this text offers a collection of papers that challenge many traditional beliefs held about the "typical" analysis of repeated measures data. I recommend this book highly." - Patrick J. Curran, University of North Carolina, Chapel Hill, USA

"Dynamical modeling of intraindividual change and variability obtained from many sources and in a wide range of time scales is the promising future of behavioral science research and the availability of this outstanding volume will accelerate our progress in that direction. Take it home, take it to the office, take it to class and, whatever you do, take it seriously!" - John R. Nesselroade, University of Virginia, USA

"Intensive longitudinal designs will be providing cutting edge insights into psychological, neuroscience and biomedical processes during the next decades, and researchers using these designs will want to study the useful approaches described in this volume. I consider this book to be required reading!" - Patrick E. Shrout, New York University, USA

"I am delighted to see this outstanding volume containing contributions connecting mathematics, classical and Bayesian statistics, signal processing and psychology. .. Other subject matter areas could well take this volume as a model for presenting the results of collaborative research in their own fields."-Robert H. Shumway, University of California, Davis, USA "This is a timely and important book that addresses an integrated set of topics that will be of great interest to a broad audience of researchers studying human dynamics. The contributors are among the leaders in their respective fields and jointly represent a truly interdisciplinary perspective on these issues. The material is presented in both an accessible and technically rigorous manner, and real data examples help clarify key points throughout. Importantly, this text offers a collection of papers that challenge many traditional beliefs held about the "typical" analysis of repeated measures data. I recommend this book highly." - Patrick J. Curran, University of North Carolina, Chapel Hill

"Dynamical modeling of intraindividual change and variability obtained from many sources and in a wide range of time scales is the promising future of behavioral science research and the availability of this outstanding volume will accelerate our progress in that direction. Take it home, take it to the office, take it to class and, whatever you do, take it seriously!" - John R. Nesselroade, University of Virginia

"Intensive longitudinal designs will be providing cutting edge insights into psychological, neuroscience and biomedical processes during the next decades, and researchers using these designs will want to study the useful approaches described in this volume. I consider this book to be required reading!" - Patrick E. Shrout, New York University

"I am delighted to see this outstanding volume containing contributions connecting mathematics, classical and Bayesian statistics, signal processing and psychology. .. Other subject matter areas could well take this volume as a model for presenting the results of collaborative research in their own fields."-Robert H. Shumway, University of California, Davis

Preface xi
Acknowledgments xv
Introduction and Section Overview
1(12)
Parametric and Exploratory Approaches for Extracting Within-Person Nonstationarities
1(3)
Representing and Extracting Intraindividual Change
4(2)
Modeling Interindividual Differences in Change and Interpersonal Dynamics
6(2)
References
8(5)
PART I: Parametric and Exploratory Approaches for Extracting Within-Person Nonstationarities
Dynamic Modeling and Optimal Control of Intraindividual Variation: A Computational Paradigm for Nonergodic Psychological Processes
13(26)
Introduction
13(1)
Ergodicity
14(2)
(Lack of) Homogeneity
16(4)
Nonstationarity
20(4)
Illustrative EKFIS Application to a Nonstationary Time Series
24(3)
A Monte Carlo Study
27(4)
Optimal Control
31(4)
Conclusion
35(1)
References
35(4)
Dynamic Spectral Analysis of Biomedical Signals with Application to Electroencephalogram and Heart Rate Variability
39(46)
Introduction
39(2)
Biomedical Signals
41(9)
Time---Frequency Representations
50(4)
Parametric Time-Varying Spectrum Estimation
54(14)
Estimation of ERS of EEG
68(4)
Estimation of HRV Dynamics During an Orthostatic Test
72(6)
Discussion
78(2)
Acknowledgments
80(1)
References
80(5)
Cluster Analysis for Nonstationary Time Series
85(38)
Introduction
85(4)
Fourier Analysis
89(3)
The WP Transform
92(6)
Clustering Nonstationary Time Series
98(5)
Simulations
103(6)
Illustrative Example
109(3)
Summary
112(1)
Acknowledgments
113(1)
Estimation of the Posterior Probability in Equation 4.4
114(1)
BBA for Selecting the Best Clustering Basis
115(2)
Model-Based Feature Selection Algorithm
117(3)
References
120(3)
Characterizing Latent Structure in Brain Signals
123(32)
Introduction
123(4)
Inferring Latent Structure via AR and TVAR Models
127(10)
Detecting Fatigue from EEGs: Experimental Setting and Data Analysis
137(13)
Conclusions and Future Directions
150(2)
Acknowledgments
152(1)
Posterior Estimation in NDLMs
152(1)
References
153(2)
A Closer Look at Two Approaches for Analysis and Classification of Nonstationary Time Series
155(6)
PART II: Representing and Extracting Intraindividual Change
Generalized Local Linear Approximation of Derivatives from Time Series
161(18)
Introduction
161(2)
Time Delay Embedding
163(2)
LLA Estimates of Derivatives
165(1)
LDE Estimates of Derivatives
165(2)
Relationship between LLA and the LDE Loading Matrix
167(2)
Simulation
169(2)
Example Application
171(2)
Example Program
173(1)
Modeling Results
174(2)
Discussion
176(1)
Conclusions
176(1)
Acknowledgments
177(1)
References
177(2)
Unbiased, Smoothing-Corrected Estimation of Oscillators in Psychology
179(34)
How do Individuals Change over Time? When? Why?
179(13)
Method for τ-Corrected Estimation of Parameters
192(2)
Estimation of ω and ρ
194(7)
Nonoscillating Time Series
201(6)
Conclusions
207(2)
Appendix 8.1
209(1)
References
210(3)
Detrending Response Time Series
213(28)
Introduction
213(4)
Motivating Series
217(3)
Detrending Methods
220(9)
A Simulation Study
229(8)
Discussion and Conclusions
237(1)
Acknowledgments
238(1)
References
239(2)
Dynamic Factor Analysis with Ordinal Manifest Variables
241(24)
Introduction
241(2)
DFA Models and their Estimation
243(3)
Polychoric Lagged Correlations
246(3)
A Simulation Study
249(6)
An Empirical Example
255(5)
Concluding Comments
260(2)
Acknowledgments
262(1)
References
262(3)
Measuring Intraindividual Variability with Intratask Change Item Response Models
265(24)
Introduction
265(4)
Intratask Change Item Response Models
269(6)
Simulations
275(2)
Example: IIV and Working Memory
277(2)
Discussion
279(4)
Acknowledgments
283(1)
References
283(6)
PART III: Modeling Interindividual Differences in Change and Interpersonal Dynamics
Developing a Random Coefficient Model for Nonlinear Repeated Measures Data
289(30)
Introduction
289(6)
Alternative Models for the MNREAD Data
295(15)
A Random Coefficient Model for the MNREAD Data
310(5)
Discussion
315(1)
The Quadratic-Linear Model with a Smooth Transition between Phases
316(1)
References
317(2)
Bayesian Discrete Dynamic System by Latent Difference Score Structural Equations Models for Multivariate Repeated Measures Data
319(30)
BE Methods
321(3)
Fitting a Univariate Latent Difference Score Model
324(6)
Fitting a Bivariate Difference Score Model
330(9)
Discussion
339(6)
References
345(4)
Longitudinal Mediation Analysis of Training Intervention Effects
349(32)
Introduction
349(1)
Mediation Analysis
350(3)
Methods for the Analysis of Training Intervention with Mediation Effects
353(9)
Empirical Data Analysis
362(14)
Conclusion and Discussion
376(2)
References
378(3)
Exploring Intraindividual, Interindividual, and Intervariable Dynamics in Dyadic Interactions
381(32)
Introduction: Dyadic Interactions
381(3)
Illustrative Data: Daily Fluctuations in Affect
384(1)
Lempell-Ziv (L-Z) Complexity
384(5)
Hierarchical Segmentation
389(9)
Stochastic Transition Networks
398(9)
Discussion
407(2)
Acknowledgment
409(1)
References
409(4)
Author Index 413(8)
Subject Index 421
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.
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