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A Framework for Investigating Change over Time |
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3 | (13) |
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When Might You Study Change over Time? |
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4 | (3) |
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Distinguishing Between Two Types of Questions about Change |
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7 | (2) |
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Three Important Features of a Study of Change |
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9 | (7) |
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Exploring Longitudinal Data on Change |
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16 | (29) |
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Creating a Longitudinal Data Set |
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17 | (6) |
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Descriptive Analysis of Individual Change over Time |
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23 | (10) |
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Exploring Differences in Change across People |
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33 | (8) |
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Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design |
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41 | (4) |
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Introducing the Multilevel Model for Change |
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45 | (30) |
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What Is the Purpose of the Multilevel Model for Change? |
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46 | (3) |
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The Level-1 Submodel for Individual Change |
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49 | (8) |
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The Level-2 Submodel for Systematic Interindividual Differences in Change |
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57 | (6) |
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Fitting the Multilevel Model for Change to Data |
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63 | (5) |
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Examining Estimated Fixed Effects |
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68 | (4) |
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Examining Estimated Variance Components |
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72 | (3) |
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Doing Data Analysis with the Multilevel Model for Change |
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75 | (63) |
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Example: Changes in Adolescent Alcohol Use |
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76 | (4) |
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The Composite Specification of the Multilevel Model for Change |
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80 | (5) |
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Methods of Estimation, Revisited |
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85 | (7) |
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First Steps: Fitting Two Unconditional Multilevel Models for Change |
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92 | (12) |
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Practical Data Analytic: Strategies for Model Building |
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104 | (12) |
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Comparing Models Using Deviance Statistics |
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116 | (6) |
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Using Wald Statistics to Test Composite Hypotheses About Fixed Effects |
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122 | (5) |
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Evaluating the Tenability of a Model's Assumptions |
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127 | (5) |
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Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters |
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132 | (6) |
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Treating TIME More Flexibly |
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138 | (51) |
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Variably Spaced Measurement Occasions |
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139 | (7) |
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Varying Numbers of Measurement Occasions |
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146 | (13) |
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159 | (22) |
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Recentering the Effect of Time |
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181 | (8) |
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Modeling Discontinuous and Nonlinear Change |
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189 | (54) |
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Discontinuous Individual Change |
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190 | (18) |
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Using Transformations to Model Nonlinear Individual Change |
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208 | (5) |
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Representing Individual Change Using a Polynomial Function of Time |
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213 | (10) |
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Truly Nonlinear Trajectories |
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223 | (20) |
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Examining the Multilevel Model's Error Covariance Structure |
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243 | (23) |
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The ``Standard'' Specification of the Multilevel Model for Change |
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243 | (3) |
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Using the Composite Model to Understand Assumptions about the Error Covariance Matrix |
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246 | (10) |
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Postulating an Alternative Error Covariance Structure |
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256 | (10) |
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Modeling Change Using Covariance Structure Analysis |
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266 | (39) |
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The General Covariance Structure Model |
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266 | (14) |
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The Basics of Latent Growth Modeling |
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280 | (15) |
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Cross-Domain Analysis of Change |
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295 | (4) |
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Extensions of Latent Growth Modeling |
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299 | (6) |
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A Framework for Investigating Event Occurrence |
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305 | (20) |
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Should You Conduct a Survival Analysis? The ``Whether'' and ``When'' Test |
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306 | (3) |
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Framing a Research Question About Event Occurrence |
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309 | (6) |
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Censoring: How Complete Are the Data on Event Occurrence? |
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315 | (10) |
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Describing Discrete-Time Event Occurrence Data |
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325 | (32) |
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326 | (4) |
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A Framework for Characterizing the Distribution of Discrete-Time Event Occurrence Data |
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330 | (9) |
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Developing Intuition About Hazard Functions, Survivor Functions, and Median Lifetimes |
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339 | (9) |
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Quantifying the Effects of Sampling Variation |
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348 | (3) |
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A Simple and Useful Strategy for Constructing the Life Table |
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351 | (6) |
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Fitting Basic Discrete-Time Hazard Models |
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357 | (50) |
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Toward a Statistical Model for Discrete-Time Hazard |
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358 | (11) |
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A Formal Representation of the Population Discrete-Time Hazard Model |
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369 | (9) |
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Fitting a Discrete-Time Hazard Model to Data |
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378 | (8) |
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Interpreting Parameter Estimates |
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386 | (5) |
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Displaying Fitted Hazard and Survivor Functions |
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391 | (6) |
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Comparing Models Using Deviance Statistics and Information Criteria |
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397 | (5) |
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Statistical Inference Using Asymptotic Standard Errors |
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402 | (5) |
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Extending the Discrete-Time Hazard Model |
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407 | (61) |
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Alternative Specifications for the ``Main Effect of TIME'' |
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408 | (11) |
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Using the Complementary Log-Log Link to Specify a Discrete-Time Hazard Model |
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419 | (7) |
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426 | (17) |
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The Linear Additivity Assumption: Uncovering Violations and Simple Solutions |
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443 | (8) |
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The Proportionality Assumption: Uncovering Violations and Simple Solutions |
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451 | (10) |
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The No Unobserved Heterogeneity Assumption: No Simple Solution |
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461 | (2) |
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463 | (5) |
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Describing Continuous-Time Event Occurrence Data |
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468 | (35) |
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A Framework for Characterizing the Distribution of Continuous-Time Event Data |
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469 | (6) |
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Grouped Methods for Estimating Continuous-Time Survivor and Hazard Functions |
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475 | (8) |
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The Kaplan-Meier Method of Estimating the Continuous-Time Survivor Function |
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483 | (5) |
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The Cumulative Hazard Function |
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488 | (6) |
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Kernel-Smoothed Estimates of the Hazard Function |
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494 | (3) |
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Developing an Intuition about Continuous-Time Survivor, Cumulative Hazard, and Kernel-Smoothed Hazard Functions |
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497 | (6) |
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Fitting Cox Regression Models |
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503 | (40) |
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Toward a Statistical Model for Continuous-Time Hazard |
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503 | (13) |
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Fitting the Cox Regression Model to Data |
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516 | (7) |
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Interpreting the Results of Fitting the Cox Regression Model to Data |
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523 | (12) |
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Nonparametric Strategies for Displaying the Results of Model Fitting |
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535 | (8) |
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Extending the Cox Regression Model |
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543 | (64) |
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544 | (12) |
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Nonproportional Hazards Models via Stratification |
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556 | (6) |
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Nonproportional Hazards Models via Interactions with Time |
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562 | (8) |
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570 | (16) |
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586 | (9) |
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Late Entry into the Risk Set |
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595 | (12) |
Notes |
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607 | (6) |
References |
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613 | (14) |
Index |
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627 | |