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E-raamat: Intensive Longitudinal Analysis of Human Processes

(Penn State University), (Penn State University, USA), (Uni of North Carolina at CH)
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Intensive Longitudinal Analysis of Human ProcessesSuurem pilt
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This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. Our purpose is to provide one consolidated resource that includes techniques from disciplines such as engineering, physics, statistics, and quantitative psychology and outlines their application to data often seen in human research. The book balances mathematical concepts with information needed for using these statistical approaches in applied settings, such as interpretative caveats and issues to consider when selecting an approach.

The statistical topics covered here include foundational material as well as state-of-the-art methods. These analytic approaches can be applied to a range of data types such as psychophysiological, self-report, and passively collected measures such as those obtained from smartphones. We provide examples using varied data sources including functional MRI (fMRI), daily diary, and ecological momentary assessment data.

Features:





Description of time series, measurement, model building, and network methods for person-specific analysis Discussion of the statistical methods in the context of human research Empirical and simulated data examples used throughout the book R code for analyses and recorded lectures for each chapter available at the book website: https://www.personspecific.com/

Across various disciplines of human study, researchers are increasingly seeking to conduct person-specific analysis. This book provides comprehensive information, so no prior knowledge of these methods is required. We aim to reach active researchers who already have some understanding of basic statistical testing. Our book provides a comprehensive resource for those who are just beginning to learn about person-specific analysis as well as those who already conduct such analysis but seek to further deepen their knowledge and learn new tools.
Preface xi
Acknowledgments xiii
About the Authors xv
Notation Used xvii
List of Abbreviations
xix
1 Introduction
1(26)
1.1 First Encounter with Intra-Individual Variation
1(9)
1.1.1 Cattell's Data Box
2(2)
1.1.2 IAV in Psychology and Related Sciences
4(2)
1.1.3 In What Areas Have the Studies of IAV Been Useful?
6(4)
1.2 Statistical Analysis of IAV: An Overview of the Structure of This Book
10(6)
1.2.1 Focus on Dynamic Factor Models
11(1)
1.2.2 Focus on Replicated Multivariate Time Series
12(1)
1.2.3 Focus on User-Friendly Model Selection and Estimation Approaches
13(1)
1.2.4 Special Topic: Methods for Dealing with Heterogeneous Replications
14(1)
1.2.5 Special Topic: Non-Stationary Dynamic Factor Models
14(1)
1.2.6 Special Topic: Control Theory
15(1)
1.2.7 Special Topic: Intersection of Network Science and IAV
15(1)
1.3 Description of Exemplar Data Sets
16(2)
1.3.1 Big Five Personality Daily Data
16(1)
1.3.2 Fisher Data
16(1)
1.3.3 The ADID Study
17(1)
1.3.4 FMRI Data
17(1)
1.4 Notation
18(1)
1.5 Conclusion
18(9)
References
18(4)
Appendix: Heuristic Introduction to Time Series Analysis for Psychologists
22(5)
2 Ergodic Theory: Mathematical Theorems about the Relation between Analysis of IAV and IEV
27(22)
2.1 Introduction
27(1)
2.2 Some History Regarding Generalizability of IEV and IAV Results
28(3)
2.3 Two Conceptualizations of Time Series
31(1)
2.4 Some Preliminaries
32(4)
2.5 When Is a System Ergodic?
36(1)
2.6 Birkhoff's Theorem of Ergodicity
37(2)
2.7 Heterogeneity as Cause of Non-Ergodicity
39(2)
2.8 Example of a Non-Ergodic Process
41(3)
2.9 Conclusion
44(5)
References
45(4)
3 P-Technique
49(24)
3.1 The P-Technique Factor Model
50(2)
3.2 The Structural Model of the Covariance Function of y(t) in P-Technique Factor Analysis
52(2)
3.3 Conducting P-Technique Factor Analysis
54(14)
3.3.1 Simulated Data
54(1)
3.3.2 Constraints for Exploratory P-Technique Factor Analysis
55(2)
3.3.3 Assessing Goodness of Fit
57(1)
3.3.4 Alternative Indices of Model Fit
58(1)
3.3.5 An Important Caveat
59(1)
3.3.5.1 The Recoverability of P-Technique
60(1)
3.3.5.2 Statistical Theory
60(1)
3.3.5.3 Concluding Thoughts
60(1)
3.3.6 Convention
61(1)
3.3.7 Determining the Number of Factors in P-Technique Factor Analysis
61(2)
3.3.8 Oblique Rotation to Simple Structure
63(1)
3.3.9 Testing the Final Oblique P-Technique Two-Factor Model
64(1)
3.3.10 Empirical Example
65(3)
3.4 Conclusion
68(5)
3.4.1 Statistical Background
68(1)
3.4.2 Application of P-Technique to Empirical Data Sets
69(1)
References
69(4)
4 Vector Autoregression (VAR)
73(30)
4.1 Brief Introduction to the Use of AR and VAR Analysis in the Study of Human Dynamics
73(1)
4.2 Elementary Linear Models for Univariate Stationary Time
74(4)
4.3 Stability and Stationarity
78(7)
4.3.1 Technical Details Regarding Stability
80(1)
4.3.2 Testing for Stability
81(1)
4.3.3 Tests for Stationarity
81(4)
4.4 Detrending Data
85(3)
4.5 Univariate Order Selection
88(2)
4.6 General VAR Model
90(3)
4.7 Multivariate Order Selection
93(2)
4.8 Testing of Residuals
95(1)
4.9 Structural Vector Autoregression
96(2)
4.10 Granger Causality
98(1)
4.11 Discussion
99(4)
References
100(3)
5 Dynamic Factor Analysis
103(28)
5.1 General Dynamic Factor Models
104(5)
5.1.1 Process Factor Analysis
105(3)
5.1.2 Shock Factor Analysis
108(1)
5.2 Lag Order Selection
109(1)
5.3 Estimation
109(19)
5.3.1 SEM Estimation with Maximum Likelihood
110(3)
5.3.1.1 Application 5.1: Exploratory SFA Estimated on Simulated Data with SEM
113(3)
5.3.1.2 Application 5.2: PFA Estimated on Simulated Data with SEM
116(2)
5.3.2 SEM with MIIV-2SLS Estimation
118(2)
5.3.2.1 Application 5.3: PFA Estimated on Simulated Data with MIIV-2SLS
120(1)
5.3.2.2 Application 5.4: PFA on fMRI Data
121(1)
5.3.3 Raw Data Likelihood Approach
121(6)
5.3.3.1 Application 5.4: PFA Estimated on Simulated Data with the Kalman Filter
127(1)
5.4 Conclusions
128(3)
References
128(3)
6 Model Specification and Selection Procedures
131(30)
6.1 Data-Driven Methods for Person-Specific Discovery of Relations among Variables
132(1)
6.2 Filter Methods
133(1)
6.3 Wrapper Methods
134(8)
6.3.1 Wald's Test
135(1)
6.3.2 Likelihood Ratio Tests
135(1)
6.3.3 Score Functions
136(1)
6.3.4 Example: Automated Relation Selection Using Wrapper Methods
137(1)
6.3.4.1 Model Search Procedure
137(2)
6.3.4.2 Simulated Data Example
139(2)
6.3.4.3 Empirical Data Example
141(1)
6.3.5 Conclusion on Wrapper Approaches
142(1)
6.4 Embedded Methods: Regularization
142(4)
6.4.1 Exemplar Approach: Regularization in Graphical VAR
144(2)
6.5 Problems with Individual-Level Searches
146(1)
6.6 Data Aggregation Approaches
147(3)
6.6.1 Exemplar Output of Aggregated Approaches
147(1)
6.6.2 Issues with Traditional Forms of Aggregation
148(2)
6.7 Replication Approaches: Group Iterative Multiple Model Estimation (GIMME)
150(6)
6.7.1 Original GIMME
151(3)
6.7.2 Hybrid GIMME
154(2)
6.8 Conclusions
156(5)
References
157(4)
7 Models of Intra-Individual Variability with Time-Varying Parameters (TVPs)
161(32)
7.1 The DFM(p,q,l,m,n) across N ≥ 1 Individuals
163(1)
7.2 The DFM(p,q,l,m,n) with TVPs as a State-Space Model
163(4)
7.3 Nonlinear State-Space Model Estimation Methods
167(6)
7.3.1 Estimation Procedures
168(1)
7.3.1.1 The Extended Kalman Filter (EKF) and the Extended Kalman Smoother (EKS)
169(2)
7.3.1.2 Parameter Estimation
171(2)
7.4 Observability and Controllability Conditions in TVPs
173(1)
7.5 Possible Functions for Representing Changes in the TVPs
174(3)
7.6 Illustrative Examples
177(11)
7.6.1 DFM Model with Time-Varying Set-Point
177(6)
7.6.2 DFM(p,q,0,1,0) with Time-Varying Set-Point and Cross-Regression Parameters
183(5)
7.7 Closing Remarks
188(5)
References
188(5)
8 Control Theory Optimization of Dynamic Processes
193(16)
8.1 Control Theory Optimization
194(4)
8.2 Illustrative Simulation
198(8)
8.3 Summary
206(3)
References
206(3)
9 The Intersection of Network Science and Intensive Longitudinal Analysis
209(28)
9.1 Terminology
209(4)
9.2 Network Measures
213(4)
9.2.1 Summarizing Edge Values: Degree, Density, Weight, and Strength
213(2)
9.2.2 Centrality Measures
215(1)
9.2.3 Measures of Segregation and Integration
216(1)
9.3 Community Detection Algorithms
217(4)
9.3.1 Walktrap
219(2)
9.4 Using Community Detection to Subgroup Individuals with Similar Dynamic Processes
221(4)
9.4.1 Exemplar Method: Subgrouping GIMME
222(2)
9.4.2 Community Detection Empirical Example: Identifying Subsets of Individuals
224(1)
9.5 Assessing Robustness of Community Detection Solutions
225(5)
9.5.1 Obtaining Random Networks
225(1)
9.5.2 Approach 1: Identifying When Solution Changes
226(4)
9.5.3 Approach 2: Evaluating Modularity
230(1)
9.6 Community Detection and P-Technique
230(4)
9.6.1 Community Detection Example: Identifying Subsets of Variables
232(2)
9.7 Discussion
234(3)
References
234(3)
Index 237
Kathleen (Katie) Gates is an associate professor of Quantitative Psychology in the Department of Psychology at the University of North Carolina at Chapel Hill. She obtained her Ph.D. in the Department of Human Development and Family Studies (quant focus) at Penn State, a Masters of Forensic Psychology at the City University of New York (John Jay College), and a BS in Psychology from Michigan State University. Katies work is motivated by problems in analyzing individual-level data. She develops algorithms and programs that may aid researchers in better quantifying behavioral, psychophysiological, and emotional processes across time. The end goal is to help researchers identify patterns within individuals so we can provide person-specific prevention, treatment, and intervention protocols as well as better understand the varied basic physiological underpinnings for emotions, cognition, and behaviors.

Sy-Miin Chow is Professor of Human Development and Family Studies at the Pennsylvania State University. She is an elected fellow of the Alexander von Humboldt Foundation in Germany and a winner of the Cattell Award from the Society for Multivariate Experimental Psychology as well as the Early Career Award from the Psychometric Society. Her work focuses on methodologies for handling intensive longitudinal data, methodological issues that arise in studies of change and human dynamics; and models and approaches for representing the dynamics of emotions, child development and family processes, as well as ways of promoting well-being and risk prevention.

Peter C. M. Molenaar is Distinguished Professor of Human Development and Family Studies at the Pennsylvania State University. He is a recipient of the Pauline Schmitt Russell Distinguished Research Career Award from the College of Health and Human Development at Penn State, the Aston Gottesman Lecture Award from the University of Virginia, the Sells Award for Distinguished Mulitvariate Research from the Society for Multivariate Experimental Psychology (SMEP), and the Tanaka Award from SMEP in 2017. His work instituted what many characterize as a conceptual and methodological paradigm-shift in the analysis of psychological, social, and behavioral processes from an inter-individual to an intra-individual variation perspective.
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