9780195152968

Applied Longitudinal Data Analysis Modeling Change and Event Occurrence

by ;
  • ISBN13:

    9780195152968

  • ISBN10:

    0195152964

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 3/27/2003
  • Publisher: Oxford University Press

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Summary

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 scoresmay 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 bookfor 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 (inboth 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

Table of Contents

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

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