Applied longitudinal data analysis : modeling change and event occurrence / Judith B. Singer, John B. Willett.

Format:

Book

Author/Creator:

Singer, Judith D.

Contributor:

Willett, John B.

Language:

English

Subjects (All):

Longitudinal method.

Social sciences--Research.

Social sciences.

Physical Description:

xx, 644 pages : illustrations ; 25 cm

Place of Publication:

Oxford ; New York : Oxford University Press, 2003.

Contents:

1 A Framework for Investigating Change over Time 3

1.1 When Might You Study Change over Time? 4

1.2 Distinguishing Between Two Types of Questions about Change 7

1.3 Three Important Features of a Study of Change 9

2 Exploring Longitudinal Data on Change 16

2.1 Creating a Longitudinal Data Set 17

2.2 Descriptive Analysis of Individual Change over Time 23

2.3 Exploring Differences in Change across People 33

2.4 Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design 41

3 Introducing the Multilevel Model for Change 45

3.1 What Is the Purpose of the Multilevel Model for Change? 46

3.2 The Level-1 Submodel for Individual Change 49

3.3 The Level-2 Submodel for Systematic Interindividual Differences in Change 57

3.4 Fitting the Multilevel Model for Change to Data 63

3.5 Examining Estimated Fixed Effects 68

3.6 Examining Estimated Variance Components 72

4 Doing Data Analysis with the Multilevel Model for Change 75

4.1 Example: Changes in Adolescent Alcohol Use 76

4.2 The Composite Specification of the Multilevel Model for Change 80

4.3 Methods of Estimation, Revisited 85

4.4 First Steps: Fitting Two Unconditional Multilevel Models for Change 92

4.5 Practical Data Analytic Strategies for Model Building 104

4.6 Comparing Models Using Deviance Statistics 116

4.7 Using Wald Statistics to Test Composite Hypotheses About Fixed Effects 122

4.8 Evaluating the Tenability of a Model's Assumptions 127

4.9 Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters 132

5 Treating TIME More Flexibly 138

5.1 Variably Spaced Measurement Occasions 139

5.2 Varying Numbers of Measurement Occasions 146

5.3 Time-Varying Predictors 159

5.4 Recentering the Effect of TIME 181

6 Modeling Discontinuous and Nonlinear Change 189

6.1 Discontinuous Individual Change 190

6.2 Using Transformations to Model Nonlinear Individual Change 208

6.3 Representing Individual Change Using a Polynomial Function of TIME 213

6.4 Truly Nonlinear Trajectories 223

7 Examining the Multilevel Model's Error Covariance Structure 243

7.1 The "Standard" Specification of the Multilevel Model for Change 243

7.2 Using the Composite Model to Understand Assumptions about the Error Covariance Matrix 246

7.3 Postulating an Alternative Error Covariance Structure 256

8 Modeling Change Using Covariance Structure Analysis 266

8.1 The General Covariance Structure Model 266

8.2 The Basics of Latent Growth Modeling 280

8.3 Cross-Domain Analysis of Change 295

8.4 Extensions of Latent Growth Modeling 299

9 A Framework for Investigating Event Occurrence 305

9.1 Should You Conduct a Survival Analysis? The "Whether" and "When" Test 306

9.2 Framing a Research Question About Event Occurrence 309

9.3 Censoring: How Complete Are the Data on Event Occurrence? 315

10 Describing Discrete-Time Event Occurrence Data 325

10.1 The Life Table 326

10.2 A Framework for Characterizing the Distribution of Discrete-Time Event Occurrence Data 330

10.3 Developing Intuition About Hazard Functions, Survivor Functions, and Median Lifetimes 339

10.4 Quantifying the Effects of Sampling Variation 348

10.5 A Simple and Useful Strategy for Constructing the Life Table 351

11 Fitting Basic Discrete-Time Hazard Models 357

11.1 Toward a Statistical Model for Discrete-Time Hazard 358

11.2 A Formal Representation of the Population Discrete-Time Hazard Model 369

11.3 Fitting a Discrete-Time Hazard Model to Data 378

11.4 Interpreting Parameter Estimates 386

11.5 Displaying Fitted Hazard and Survivor Functions 391

11.6 Comparing Models Using Deviance Statistics and Information Criteria 397

11.7 Statistical Inference Using Asymptotic Standard Errors 402

12 Extending the Discrete-Time Hazard Model 407

12.1 Alternative Specifications for the "Main Effect of TIME" 408

12.2 Using the Complementary Log-Log Link to Specify a Discrete-Time Hazard Model 419

12.3 Time-Varying Predictors 426

12.4 The Linear Additivity Assumption: Uncovering Violations and Simple Solutions 443

12.5 The Proportionality Assumption: Uncovering Violations and Simple Solutions 451

12.6 The No Unobserved Heterogeneity Assumption: No Simple Solution 461

12.7 Residual Analysis 463

13 Describing Continuous-Time Event Occurrence Data 468

13.1 A Framework for Characterizing the Distribution of Continuous-Time Event Data 469

13.2 Grouped Methods for Estimating Continuous-Time Survivor and Hazard Functions 475

13.3 The Kaplan-Meier Method of Estimating the Continuous-Time Survivor Function 483

13.4 The Cumulative Hazard Function 488

13.5 Kernel-Smoothed Estimates of the Hazard Function 494

13.6 Developing an Intuition about Continuous-Time Survivor, Cumulative Hazard, and Kernel-Smoothed Hazard Functions 497

14 Fitting Cox Regression Models 503

14.1 Toward a Statistical Model for Continuous-Time Hazard 503

14.2 Fitting the Cox Regression Model to Data 516

14.3 Interpreting the Results of Fitting the Cox Regression Model to Data 523

14.4 Nonparametric Strategies for Displaying the Results of Model Fitting 535

15 Extending the Cox Regression Model 543

15.1 Time-Varying Predictors 544

15.2 Nonproportional Hazards Models via Stratification 556

15.3 Nonproportional Hazards Models via Interactions with Time 562

15.4 Regression Diagnostics 570

15.5 Competing Risks 586

15.6 Late Entry into the Risk Set 595.

Notes:

Includes bibliographical references (pages 613-626) and index.

ISBN:

0195152964

OCLC:

49773716