Applied Longitudinal Data Analysis
Modeling Change and Event Occurrence
1st Edition
Published on 27. March 2003
665 pages
978-0-19-976072-5 (ISBN)
Description
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. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.
Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods.
Visit http://www.ats.ucla.edu/stat/examples/alda.htm for:
BL Downloadable data sets
BL Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
BL Additional material for data analysis
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Other editions
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Judith D. Singer | John B. Willett
Applied Longitudinal Data Analysis
Modeling Change and Event Occurrence
Book
05/2003
Oxford University Press
€166.50
Shipment within 15-20 days
Content
- Cover
- Contents
- PART I
- 1 A Framework for Investigating Change over Time
- 1.1 When Might You Study Change over Time?
- 1.2 Distinguishing Between Two Types of Questions about Change
- 1.3 Three Important Features of a Study of Change
- 2 Exploring Longitudinal Data on Change
- 2.1 Creating a Longitudinal Data Set
- 2.2 Descriptive Analysis of Individual Change over Time
- 2.3 Exploring Differences in Change across People
- 2.4 Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design
- 3 Introducing the Multilevel Model for Change
- 3.1 What Is the Purpose of the Multilevel Model for Change?
- 3.2 The Level-1 Submodel for Individual Change
- 3.3 The Level-2 Submodel for Systematic Interindividual Differences in Change
- 3.4 Fitting the Multilevel Model for Change to Data
- 3.5 Examining Estimated Fixed Effects
- 3.6 Examining Estimated Variance Components
- 4 Doing Data Analysis with the Multilevel Model for Change
- 4.1 Example: Changes in Adolescent Alcohol Use
- 4.2 The Composite Specification of the Multilevel Model for Change
- 4.3 Methods of Estimation, Revisited
- 4.4 First Steps: Fitting Two Unconditional Multilevel Models for Change
- 4.5 Practical Data Analytic Strategies for Model Building
- 4.6 Comparing Models Using Deviance Statistics
- 4.7 Using Wald Statistics to Test Composite Hypotheses About Fixed Effects
- 4.8 Evaluating the Tenability of a Model's Assumptions
- 4.9 Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters
- 5 Treating TIME More Flexibly
- 5.1 Variably Spaced Measurement Occasions
- 5.2 Varying Numbers of Measurement Occasions
- 5.3 Time-Varying Predictors
- 5.4 Recentering the Effect of TIME
- 6 Modeling Discontinuous and Nonlinear Change
- 6.1 Discontinuous Individual Change
- 6.2 Using Transformations to Model Nonlinear Individual Change
- 6.3 Representing Individual Change Using a Polynomial Function of TIME
- 6.4 Truly Nonlinear Trajectories
- 7 Examining the Multilevel Model's Error Covariance Structure
- 7.1 The "Standard" Specification of the Multilevel Model for Change
- 7.2 Using the Composite Model to Understand Assumptions about the Error Covariance Matrix
- 7.3 Postulating an Alternative Error Covariance Structure
- 8 Modeling Change Using Covariance Structure Analysis
- 8.1 The General Covariance Structure Model
- 8.2 The Basics of Latent Growth Modeling
- 8.3 Cross-Domain Analysis of Change
- 8.4 Extensions of Latent Growth Modeling
- PART II
- 9 A Framework for Investigating Event Occurrence
- 9.1 Should You Conduct a Survival Analysis? The "Whether" and "When" Test
- 9.2 Framing a Research Question About Event Occurrence
- 9.3 Censoring: How Complete Are the Data on Event Occurrence?
- 10 Describing Discrete-Time Event Occurrence Data
- 10.1 The Life Table
- 10.2 A Framework for Characterizing the Distribution of Discrete-Time Event Occurrence Data
- 10.3 Developing Intuition About Hazard Functions, Survivor Functions, and Median Lifetimes
- 10.4 Quantifying the Effects of Sampling Variation
- 10.5 A Simple and Useful Strategy for Constructing the Life Table
- 11 Fitting Basic Discrete-Time Hazard Models
- 11.1 Toward a Statistical Model for Discrete-Time Hazard
- 11.2 A Formal Representation of the Population Discrete-Time Hazard Model
- 11.3 Fitting a Discrete-Time Hazard Model to Data
- 11.4 Interpreting Parameter Estimates
- 11.5 Displaying Fitted Hazard and Survivor Functions
- 11.6 Comparing Models Using Deviance Statistics and Information Criteria
- 11.7 Statistical Inference Using Asymptotic Standard Errors
- 12 Extending the Discrete-Time Hazard Model
- 12.1 Alternative Specifications for the "Main Effect of TIME"
- 12.2 Using the Complementary Log-Log Link to Specify a Discrete-Time Hazard Model
- 12.3 Time-Varying Predictors
- 12.4 The Linear Additivity Assumption: Uncovering Violations and Simple Solutions
- 12.5 The Proportionality Assumption: Uncovering Violations and Simple Solutions
- 12.6 The No Unobserved Heterogeneity Assumption: No Simple Solution
- 12.7 Residual Analysis
- 13 Describing Continuous-Time Event Occurrence Data
- 13.1 A Framework for Characterizing the Distribution of Continuous-Time Event Data
- 13.2 Grouped Methods for Estimating Continuous-Time Survivor and Hazard Functions
- 13.3 The Kaplan-Meier Method of Estimating the Continuous-Time Survivor Function
- 13.4 The Cumulative Hazard Function
- 13.5 Kernel-Smoothed Estimates of the Hazard Function
- 13.6 Developing an Intuition about Continuous-Time Survivor, Cumulative Hazard, and Kernel-Smoothed Hazard Functions
- 14 Fitting Cox Regression Models
- 14.1 Toward a Statistical Model for Continuous-Time Hazard
- 14.2 Fitting the Cox Regression Model to Data
- 14.3 Interpreting the Results of Fitting the Cox Regression Model to Data
- 14.4 Nonparametric Strategies for Displaying the Results of Model Fitting
- 15 Extending the Cox Regression Model
- 15.1 Time-Varying Predictors
- 15.2 Nonproportional Hazards Models via Stratification
- 15.3 Nonproportional Hazards Models via Interactions with Time
- 15.4 Regression Diagnostics
- 15.5 Competing Risks
- 15.6 Late Entry into the Risk Set
- Notes
- References
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
