Applied longitudinal data analysis: modeling change and event occurrence
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford [u.a.]
Oxford Univ. Press
2003
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XX, 644 S. graph. Darst. |
| ISBN: | 9780195152968 |
Staff View
MARC
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| 245 | 1 | 0 | |a Applied longitudinal data analysis |b modeling change and event occurrence |c Judith D. Singer ; John B. Willett |
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2003 | |
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| 650 | 4 | |a Sciences sociales - Recherche | |
| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Longitudinal method | |
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Record in the Search Index
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| adam_text | Contents
PART I
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
Prensión
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 Modehfor Change
92
xviii Contents
4.5
Practical
Data
Analytic Strategies for
Model Building 104
4.6
Comparing Modeb 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 o/^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
PART II
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
Contents xix
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
xx Contents
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 ModeL· 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
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 chart¬
ing 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 re¬
searchers and graduate students in the behav¬
ioral, 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 underly¬
ing patterns through sophisticated specifi¬
cations of complex statistical models. To insure
that readers can apply the methods on their
own, the book s companion website presents
data sets and computer code in a wide variety
of software packages: http://www.oup-usa.
org/alda.
Applied Longitudinal Data Analysis offers read¬
ers a private consultation session with inter¬
nationally recognized experts and represents a
unique contribution to the literature on quan¬
titative empirical methods.
About the Authors
Judith D. Singer is James Bryant Conant
Professor and John B. Willett is Charles
William Eliot Professor at Harvard University,
Graduate School of Education. Longtime
collaborators, both authors specialize in
applied statistical methods and research
design, particularly for longitudinal data.
Jacket design: Ed Atkeson/Berg Design
Jacket art: Corbis Royalty-Free
OXFORD
UNIVERSITY PRESS
www.oup.com
|
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| illustrated | Illustrated |
| indexdate | 2024-07-09T19:03:08Z |
| institution | BVB |
| isbn | 9780195152968 |
| language | English |
| lccn | 2002007055 |
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| physical | XX, 644 S. graph. Darst. |
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| spelling | Singer, Judith D. 1955- Verfasser (DE-588)172404568 aut Applied longitudinal data analysis modeling change and event occurrence Judith D. Singer ; John B. Willett Oxford [u.a.] Oxford Univ. Press 2003 XX, 644 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Méthode longitudinale Sciences sociales - Recherche Sozialwissenschaften Longitudinal method Social sciences Research Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Statistik (DE-588)4056995-0 s DE-604 Längsschnittuntersuchung (DE-588)4034036-3 s Sozialwissenschaften (DE-588)4055916-6 s DE-188 Willett, John B. 1947- Verfasser (DE-588)131401475 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009883792&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009883792&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Singer, Judith D. 1955- Willett, John B. 1947- Applied longitudinal data analysis modeling change and event occurrence Méthode longitudinale Sciences sociales - Recherche Sozialwissenschaften Longitudinal method Social sciences Research Längsschnittuntersuchung (DE-588)4034036-3 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| subject_GND | (DE-588)4034036-3 (DE-588)4056995-0 (DE-588)4055916-6 |
| title | Applied longitudinal data analysis modeling change and event occurrence |
| title_auth | Applied longitudinal data analysis modeling change and event occurrence |
| title_exact_search | Applied longitudinal data analysis modeling change and event occurrence |
| title_full | Applied longitudinal data analysis modeling change and event occurrence Judith D. Singer ; John B. Willett |
| title_fullStr | Applied longitudinal data analysis modeling change and event occurrence Judith D. Singer ; John B. Willett |
| title_full_unstemmed | Applied longitudinal data analysis modeling change and event occurrence Judith D. Singer ; John B. Willett |
| title_short | Applied longitudinal data analysis |
| title_sort | applied longitudinal data analysis modeling change and event occurrence |
| title_sub | modeling change and event occurrence |
| topic | Méthode longitudinale Sciences sociales - Recherche Sozialwissenschaften Longitudinal method Social sciences Research Längsschnittuntersuchung (DE-588)4034036-3 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| topic_facet | Méthode longitudinale Sciences sociales - Recherche Sozialwissenschaften Longitudinal method Social sciences Research Längsschnittuntersuchung Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009883792&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009883792&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT singerjudithd appliedlongitudinaldataanalysismodelingchangeandeventoccurrence AT willettjohnb appliedlongitudinaldataanalysismodelingchangeandeventoccurrence |