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| A Framework for Investigating Change over Time | |
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| When Might You Study Change over Time? | |
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| Distinguishing Between Two Types of Questions about Change | |
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| Three Important Features of a Study of Change | |
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| Exploring Longitudinal Data on Change | |
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| Creating a Longitudinal Data Set | |
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| Descriptive Analysis of Individual Change over Time | |
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| Exploring Differences in Change across People | |
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| Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design | |
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| Introducing the Multilevel Model for Change | |
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| What Is the Purpose of the Multilevel Model for Change? | |
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| The Level-1 Submodel for Individual Change | |
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| The Level-2 Submodel for Systematic Interindividual Differences in Change | |
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| Fitting the Multilevel Model for Change to Data | |
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| Examining Estimated Fixed Effects | |
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| Examining Estimated Variance Components | |
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| Doing Data Analysis with the Multilevel Model for Change | |
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| Example: Changes in Adolescent Alcohol Use | |
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| The Composite Specification of the Multilevel Model for Change | |
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| Methods of Estimation, Revisited | |
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| First Steps: Fitting Two Unconditional Multilevel Models for Change | |
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| Practical Data Analytic Strategies for Model Building | |
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| Comparing Models Using Deviance Statistics | |
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| Using Wald Statistics to Test Composite Hypotheses About Fixed Effects | |
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| Evaluating the Tenability of a Model's Assumptions | |
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| Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters | |
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| Treating TIME More Flexibly | |
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| Variably Spaced Measurement Occasions | |
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| Varying Numbers of Measurement Occasions | |
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| Time-Varying Predictors | |
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| Recentering the Effect of TIME | |
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| Modeling Discontinuous and Nonlinear Change | |
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| Discontinuous Individual Change | |
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| Using Transformations to Model Nonlinear Individual Change | |
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| Representing Individual Change Using a Polynomial Function of TIME | |
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| Truly Nonlinear Trajectories | |
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| Examining the Multilevel Model's Error Covariance Structure | |
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| The "Standard" Specification of the Multilevel Model for Change | |
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| Using the Composite Model to Understand Assumptions about the Error Covariance Matrix | |
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| Postulating an Alternative Error Covariance Structure | |
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| Modeling Change Using Covariance Structure Analysis | |
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| The General Covariance Structure Model | |
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| The Basics of Latent Growth Modeling | |
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| Cross-Domain Analysis of Change | |
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| Extensions of Latent Growth Modeling | |
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| A Framework for Investigating Event Occurrence | |
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| Should You Conduct a Survival Analysis? The "Whether" and "When" Test | |
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| Framing a Research Question About Event Occurrence | |
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| Censoring: How Complete Are the Data on Event Occurrence? | |
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| Describing Discrete-Time Event Occurrence Data | |
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| The Life Table | |
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| A Framework for Characterizing the Distribution of Discrete-Time Event Occurrence Data | |
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| Developing Intuition About Hazard Functions, Survivor Functions, and Median Lifetimes | |
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| Quantifying the Effects of Sampling Variation | |
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| A Simple and Useful Strategy for Constructing the Life Table | |
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| Fitting Basic Discrete-Time Hazard Models | |
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| Toward a Statistical Model for Discrete-Time Hazard | |
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| A Formal Representation of the Population Discrete-Time Hazard Model | |
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| Fitting a Discrete-Time Hazard Model to Data | |
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| Interpreting Parameter Estimates | |
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| Displaying Fitted Hazard and Survivor Functions | |
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| Comparing Models Using Deviance Statistics and Information Criteria | |
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| Statistical Inference Using Asymptotic Standard Errors | |
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| Extending the Discrete-Time Hazard Model | |
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| Alternative Specifications for the "Main Effect of TIME" | |
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| Using the Complementary Log-Log Link to Specify a Discrete-Time Hazard Model | |
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| Time-Varying Predictors | |
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| The Linear Additivity Assumption: Uncovering Violations and Simple Solutions | |
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| The Proportionality Assumption: Uncovering Violations and Simple Solutions | |
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| The No Unobserved Heterogeneity Assumption: No Simple Solution | |
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| Residual Analysis | |
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| Describing Continuous-Time Event Occurrence Data | |
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| A Framework for Characterizing the Distribution of Continuous-Time Event Data | |
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| Grouped Methods for Estimating Continuous-Time Survivor and Hazard Functions | |
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| The Kaplan-Meier Method of Estimating the Continuous-Time Survivor Function | |
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| The Cumulative Hazard Function | |
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| Kernel-Smoothed Estimates of the Hazard Function | |
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| Developing an Intuition about Continuous-Time Survivor, Cumulative Hazard, and Kernel-Smoothed Hazard Functions | |
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| Fitting Cox Regression Models | |
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| Toward a Statistical Model for Continuous-Time Hazard | |
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| Fitting the Cox Regression Model to Data | |
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| Interpreting the Results of Fitting the Cox Regression Model to Data | |
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| Nonparametric Strategies for Displaying the Results of Model Fitting | |
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| Extending the Cox Regression Model | |
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| Time-Varying Predictors | |
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| Nonproportional Hazards Models via Stratification | |
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| Nonproportional Hazards Models via Interactions with Time | |
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| Regression Diagnostics | |
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| Competing Risks | |
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| Late Entry into the Risk Set | |
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| Notes | |
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| References | |
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| Index | |