Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Front Cover
Oxford University Press, USA, Mar 27, 2003 - Mathematics - 644 pages
4 Reviews
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:
DT Downloadable data sets
DT Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
DT Additional material for data analysis
 

What people are saying - Write a review

LibraryThing Review

User Review  - tsryan - LibraryThing

I often find statistics textbooks largely incomprehensible, so it was a nice surprise to find this one, dedicated to a somewhat advanced sub-topic within statistics, to be a relatively easy read. It ... Read full review

Contents

A Framework for Investigating Change over Time
3
11 When Might You Study Change over Time?
4
12 Distinguishing Between Two Types of Questions about Change
7
13 Three Important Features of a Study of Change
9
Exploring Longitudinal Data on Change
16
21 Creating a Longitudinal Data Set
17
22 Descriptive Analysis of Individual Change over Time
23
23 Exploring Differences in Change across People
33
92 Framing a Research Question about Event Occurrence
309
How Complete Are the Data on Event Occurrence?
315
Describing DiscreteTime Event Occurrence Data
325
101 The Life Table
326
102 A Framework for Characterizing the Distribution of DiscreteTime Event Occurrence Data
330
103 Developing Intuition About Hazard Functions Survivor Functions and Median Lifetimes
339
104 Quantifying the Effects of Sampling Variation
348
105 A Simple and Useful Strategy for Constructing the Life Table
351

Lessons for Research Design
41
Introducing the Multilevel Model for Change
45
31 What Is the Purpose of the Multilevel Model for Change?
46
32 The Level1 Submodel for Individual Change
49
33 The Level2 Submodel for Systematic Interindividual Differences in Change
57
34 Fitting the Multilevel Model for Change to Data
63
35 Examining Estimated Fixed Effects
68
36 Examining Estimated Variance Components
72
Doing Data Analysis with the Multilevel Model for Change
75
Changes in Adolescent Alcohol Use
76
42 The Composite Specification of the Multilevel Model for Change
80
43 Methods of Estimation Revisited
85
Fitting Two Unconditional Multilevel Models for Change
92
45 Practical Data Analytic Strategies for Model Building
104
46 Comparing Models Using Deviance Statistics
116
47 Using Wald Statistics to Test Composite Hypotheses About Fixed Effects
122
48 Evaluating the Tenability of a Models Assumptions
127
49 ModelBased Empirical Bayes Estimates of the Individual Growth Parameters
132
Treating TIME More Flexibly
138
51 Variably Spaced Measurement Occasions
139
52 Varying Numbers of Measurement Occasions
146
53 TimeVarying Predictors
159
54 Recentering the Effect of TIME
181
Modeling Discontinuous and Nonlinear Change
189
61 Discontinuous Individual Change
190
62 Using Transformations to Model Nonlinear Individual Change
208
63 Representing Individual Change Using a Polynomial Function of TIME
213
64 Truly Nonlinear Trajectories
223
Examining the Multilevel Models Error Covariance Structure
243
Assumptions about the Error Covariance Matrix
246
73 Postulating an Alternative Error Covariance Structure
256
Modeling Change Using Covariance Structure Analysis
266
82 The Basics of Latent Growth Modeling
280
83 CrossDomain Analysis of Change
295
84 Extensions of Latent Growth Modeling
299
PART II
303
A Framework for Investigating Event Occurrence
305
91 Should You Conduct a Survival Analysis? The Whether and When Test
306
Fitting Basic DiscreteTime Hazard Models
357
111 Toward a Statistical Model for DiscreteTime Hazard
358
112 A Formal Representation of the Population DiscreteTime Hazard Model
369
113 Fitting a DiscreteTime Hazard Model to Data
378
114 Interpreting Parameter Estimates
386
115 Displaying Fitted Hazard and Survivor Functions
391
116 Comparing Models Using Deviance Statistics and Information Criteria
397
117 Statistical Inference Using Asymptotic Standard Errors
402
Extending the DiscreteTime Hazard Model
407
121 Alternative Specifications for the Main Effect of TIME
408
122 Using the Complementary LogLog Link to Specify a DiscreteTime Hazard Model
419
123 TimeVarying Predictors
426
Uncovering Violations and Simple Solutions
443
Uncovering Violations and Simple Solutions
451
No Simple Solution
461
127 Residual Analysis
463
Describing ContinuousTime Event Occurrence Data
468
131 A Framework for Characterizing the Distribution of ContinuousTime Event Data
469
132 Grouped Methods for Estimating ContinuousTime Survivor and Hazard Functions
475
133 The KaplanMeier Method of Estimating the ContinuousTime Survivor Function
483
134 The Cumulative Hazard Function
488
135 KernelSmoothed Estimates of the Hazard Function
494
Survivor Cumulative Hazard and KernelSmoothed Hazard Functions
497
Fitting Cox Regression Models
503
142 Fitting the Cox Regression Model to Data
516
143 Interpreting the Results of Fitting the Cox Regression Model to Data
523
144 Nonparametric Strategies for Displaying the Results of Model Fitting
535
Extending the Cox Regression Model
543
151 TimeVarying Predictors
544
152 Nonproportional Hazards Models via Stratification
556
153 Nonproportional Hazards Models via Interactions with Time
562
154 Regression Diagnostics
570
155 Competing Risks
586
156 Late Entry into the Risk Set
595
Notes
607
References
613
Index
627
Copyright

Other editions - View all

Common terms and phrases

Popular passages

Page 615 - DA (1996). Temporal tempering: An event history analysis of the process of voluntary turnover.

References to this book

All Book Search results »

About the author (2003)

John B. Willett is at Harvard University.

Bibliographic information