Intensive longitudinal analysis of human processes. (2022)

• Record Type: Book
• Further Information: Note: Kathleen Gates, Sy-Miin Chow, Peter C.M. Molenaar.
• Authors: Gates, Kathleen; Chow, Sy-Miin; Molenaar, Peter C. M
• Contents: 1. Introduction 1.1 First encounter with intra-individual variation. 1.2 Statistical Analysis of IAV: An overview of the structure of this book. 1.3 Description of exemplar data sets.1.4 Notation. 1.5 Conclusions. 2. Ergodic Theory: Mathematical theorems about the relation between IAV and IEV. 2.1 Introduction. 2.2 Some history regarding generalizability of IEV and IAV results. 2.3 Two conceptualizations of time series. 2.4 Some preliminaries. 2.5 Birkhoff’s theorem of ergodicity. 2.6 When is a system ergodic? 2.7 Heterogeneity as cause of non-ergodicity. 2.8 Example of a non-ergodic process. 2.9 Conclusions. 3. P-Technique. 3.1 The P-Technique Factor model. 3.2 The structural model of the covariance function of y(t) in P-technique factor analysis. 3.3 Conducting P-technique factor analysis. 3.4 Conclusions. 4. Vector Autoregression (VAR). 4.1 Brief introduction on the use of AR and VAR analysis in the study of human dynamics. 4.2 Elementary linear models for univariate stationary time. 4.3 Stability and stationarity. 4.4 Detrending data. 4.5 Univariate order selection. 4.6 General VAR model. 4.7 Multivariate order selection. 4.8 Testing of residuals. 4.9 Structural vector autoregression. 4.10 Granger causality. 4.11 Discussion. 5. Dynamic Factor Analysis. 5.1 General dynamic factor models. 5.2 Lag order selection. 5.3 Estimation. 5.4 Conclusions. 6. Model Specification and Selection Procedures. 6.1 Data-driven methods for person-specific discovery of relations among variables. 6.2 Filter methods. 6.3 Wrapper methods. 6.4 Embedded methods: Regularization. 6.5 Problems with individual-level searches. 6.6 Data aggregation approaches. 6.7 Group Iterative Multiple Model Estimation (GIMME) Approaches. 6.8 Conclusions. 7. Models of Intraindividual Variability with Time-Varying Parameters (TVPs). 7.1 The DFM(p, q, l, m, m ) across N≥ individuals. 7.2 The DFM(p, q, l, m, m ) with TVPs as a state-space model. 7.3 Nonlinear state-space model estimation methods. 7.4 Observability and controllability conditions in TVPs. 7.5 Possible functions for representing changes in the TVPs. 7.6 Illustrative examples. 7.7 Closing remarks. 8. Control Theory Optimization of Dynamic Processes. 8.1 Control theory optimization. 8.2 Illustrative simulation. 8.3 Summary. 9. The Intersection of Network Science and IAV. 9.1 Terminology. 9.2 Network measures. 9.3 Community detection algorithms. 9.4 Using community detection to subgroup individuals with similar dynamic processes. 9.5 Assessing robustness of community detection solutions. 9.6 Community detection and P-technique. 9.7 Discussion.
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 150.15195; Variability (Psychometrics); Psychology -- Statistical methods; Human behavior -- Mathematical models
• Languages: English
• ISBNs: 9780429528927; 9781482230604; 9780429172649
• Related ISBNs: 9781482230598
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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