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The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal data. This much-needed professional book will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modeling and survival analysis, making it a unique contribution to the literature on research methods. The authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard models, continuous-time event occurrence, and Cox regression models. The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal data. This much-needed professional book will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modeling and survival analysis, making it a unique contribution to the literature on research methods. The authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard models, continuous-time event occurrence, and Cox regression models.

Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. In addition to these natural changes, targeted interventions may cause change: cholesterol levels may decline as a result of a new medication, exam grades may rise following completion of a coaching class. By measuring and charting changes like these - both naturalistic and experimentally induced - researchers uncover the temporal nature of development. The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal data. Applied Longitudinal Data Analysis is a much-needed professional book that will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modelling and survival analysis. Throughout the chapters, the authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard models, continuous-time event occurrence, and Cox regression models. Applied Longitudinal Data Analysis is a unique contribution to the literature on research methods and will be useful to a wide range of behavioural and social science researchers. Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. In addition to these natural changes, targeted interventions may cause change: cholesterol levels may decline as a result of a new medication, exam grades may rise following completion of a coaching class. By measuring and charting changes like these - both naturalistic and experimentally induced - researchers uncover the temporal nature of development. The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal data. Applied Longitudinal Data Analysis is a much-needed professional book that will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modelling and survival analysis. Throughout the chapters, the authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard models, continuous-time event occurrence, and Cox regression models. Applied Longitudinal Data Analysis is a unique contribution to the literature on research methods and will be useful to a wide range of behavioural and social science researchers.

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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		607
References		613
Index		627