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Intro; Preface; Contents; Contributors; 1 First- and Higher-Order Continuous Time Models for Arbitrary N Using SEM; 1.1 Introduction; 1.2 Continuous Time Model; 1.2.1 Basic Model; 1.2.2 Connecting Discrete and Continuous Time Model in the EDM; 1.2.3 Extended Continuous Time Model; 1.2.4 Exogenous Variables; 1.2.5 Traits; 1.3 Model Estimation by SEM; 1.4 Analysis of Sunspot Data: CARMA(2,1) on N = 1, T = 167; 1.5 Conclusion; References; 2 A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How?; 2.1 Introduction; 2.2 Two Frameworks; 2.2.1 The Discrete-Time Framework
2.2.2 The Continuous-Time Framework2.2.3 Relating DT and CT Models; 2.2.4 Types of Dynamics: Eigenvalues, Stability, and Equilibrium; 2.3 Why Researchers Should Adopt a CT Process Perspective; 2.4 Making Sense of CT Models; 2.4.1 Substantive Example from Empirical Data; 2.4.2 Interpreting the Drift Parameters; 2.4.3 Visualizing Trajectories; 2.4.3.1 Impulse Response Functions; 2.4.3.2 Vector Fields; 2.4.4 Inspecting the Lagged Parameters; 2.4.5 Caution with Interpreting Estimated Parameters; 2.5 Discussion; 2.5.1 Beyond Two-Dimensional Systems; 2.5.2 Complex and Positive Eigenvalues
2.5.3 Multilevel Extensions2.5.4 Conclusion; Appendix: Matrix Exponential; References; 3 On Fitting a Continuous-Time Stochastic Process Model in the Bayesian Framework; 3.1 Introduction; 3.1.1 The Need for Continuous-Time Process Models to Analyze Intensive Longitudinal Data; 3.1.2 The Need for Continuous-Time Process Models to Capture Temporal Changes in Core Affective States; 3.2 The Ornstein-Uhlenbeck Process to Describe Within-Person Latent Temporal Dynamics; 3.2.1 The Stochastic Differential Equation Definition of the Ornstein-Uhlenbeck Process
3.2.2 The Position Equation of the Ornstein-Uhlenbeck Process3.2.3 Extending the Ornstein-Uhlenbeck Process to Two Dimensions; 3.2.4 Accounting for Measurement Error; 3.3 A Multilevel/Hierarchical Extension to the Ornstein-Uhlenbeck Process; 3.3.1 Specifying the Population Distribution for the Baseline; 3.3.2 Specifying the Population Distribution for the Regulatory Force; 3.3.3 Specifying the Population Distribution for the BPS Input; 3.4 Casting the Multilevel OU Process Model in the Bayesian Framework
3.5 Investigating Core Affect Dynamics with the Bayesian Multilevel Ornstein-Uhlenbeck Process Model3.5.1 A Process Model of Core Affect Dynamics Measured in an Ecological Momentary Assessment Study; 3.5.2 Population-Level Summaries and Individual Differences of Core Affect Dynamics; 3.5.3 Results on the Time-Invariant Covariates; 3.6 Discussion; References; 4 Understanding the Time Course of Interventions with Continuous Time Dynamic Models; 4.1 Introduction; 4.2 The Model; 4.2.1 Latent Dynamic Model; 4.2.2 Discrete Time Solution of Latent Dynamic Model; 4.2.3 Measurement Model
2.2.2 The Continuous-Time Framework2.2.3 Relating DT and CT Models; 2.2.4 Types of Dynamics: Eigenvalues, Stability, and Equilibrium; 2.3 Why Researchers Should Adopt a CT Process Perspective; 2.4 Making Sense of CT Models; 2.4.1 Substantive Example from Empirical Data; 2.4.2 Interpreting the Drift Parameters; 2.4.3 Visualizing Trajectories; 2.4.3.1 Impulse Response Functions; 2.4.3.2 Vector Fields; 2.4.4 Inspecting the Lagged Parameters; 2.4.5 Caution with Interpreting Estimated Parameters; 2.5 Discussion; 2.5.1 Beyond Two-Dimensional Systems; 2.5.2 Complex and Positive Eigenvalues
2.5.3 Multilevel Extensions2.5.4 Conclusion; Appendix: Matrix Exponential; References; 3 On Fitting a Continuous-Time Stochastic Process Model in the Bayesian Framework; 3.1 Introduction; 3.1.1 The Need for Continuous-Time Process Models to Analyze Intensive Longitudinal Data; 3.1.2 The Need for Continuous-Time Process Models to Capture Temporal Changes in Core Affective States; 3.2 The Ornstein-Uhlenbeck Process to Describe Within-Person Latent Temporal Dynamics; 3.2.1 The Stochastic Differential Equation Definition of the Ornstein-Uhlenbeck Process
3.2.2 The Position Equation of the Ornstein-Uhlenbeck Process3.2.3 Extending the Ornstein-Uhlenbeck Process to Two Dimensions; 3.2.4 Accounting for Measurement Error; 3.3 A Multilevel/Hierarchical Extension to the Ornstein-Uhlenbeck Process; 3.3.1 Specifying the Population Distribution for the Baseline; 3.3.2 Specifying the Population Distribution for the Regulatory Force; 3.3.3 Specifying the Population Distribution for the BPS Input; 3.4 Casting the Multilevel OU Process Model in the Bayesian Framework
3.5 Investigating Core Affect Dynamics with the Bayesian Multilevel Ornstein-Uhlenbeck Process Model3.5.1 A Process Model of Core Affect Dynamics Measured in an Ecological Momentary Assessment Study; 3.5.2 Population-Level Summaries and Individual Differences of Core Affect Dynamics; 3.5.3 Results on the Time-Invariant Covariates; 3.6 Discussion; References; 4 Understanding the Time Course of Interventions with Continuous Time Dynamic Models; 4.1 Introduction; 4.2 The Model; 4.2.1 Latent Dynamic Model; 4.2.2 Discrete Time Solution of Latent Dynamic Model; 4.2.3 Measurement Model