HOME > 상세정보

상세정보

Applied longitudinal data analysis : modeling change and event occurrence

Applied longitudinal data analysis : modeling change and event occurrence (76회 대출)

자료유형
단행본
개인저자
Singer, Judith D. Willett, John B.
서명 / 저자사항
Applied longitudinal data analysis : modeling change and event occurrence / by Judith B. Singer and John B. Willett.
발행사항
New York :   Oxford University Press,   c2003.  
형태사항
xx, 644 p. ; 24 cm.
ISBN
0195152964 (acid-free) 9780195152968
서지주기
Includes bibliographical references and index.
일반주제명
Longitudinal method. Social sciences --Research.
000 00000cam u2200205 a 4500
001 000045115765
005 20170925163141
008 020506s2003 nyu b 001 0 eng d
010 ▼a 02007055
020 ▼a 0195152964 (acid-free)
020 ▼a 9780195152968
040 ▼a DLC ▼c DLC ▼d 211009 ▼d 244002
050 0 0 ▼a H62 ▼b .S47755 2002
082 0 0 ▼a 001.4/2 ▼2 23
084 ▼a 001.42 ▼2 DDCK
090 ▼a 001.42 ▼b S617a
100 1 ▼a Singer, Judith D.
245 1 0 ▼a Applied longitudinal data analysis : ▼b modeling change and event occurrence / ▼c by Judith B. Singer and John B. Willett.
260 ▼a New York : ▼b Oxford University Press, ▼c c2003.
300 ▼a xx, 644 p. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Longitudinal method.
650 0 ▼a Social sciences ▼x Research.
700 1 ▼a Willett, John B.

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 001.42 S617a 등록번호 121241712 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 의학도서관/자료실(3층)/ 청구기호 001.42 S617a 등록번호 131023226 도서상태 대출가능 반납예정일 예약 서비스 B
No. 3 소장처 의학도서관/자료실(3층)/ 청구기호 001.42 S617a 등록번호 131050215 도서상태 대출가능 반납예정일 예약 서비스 B
No. 4 소장처 세종학술정보원/인문자료실1/ 청구기호 001.42 S617a 등록번호 151300188 도서상태 대출가능 반납예정일 예약 서비스
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 001.42 S617a 등록번호 121241712 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 의학도서관/자료실(3층)/ 청구기호 001.42 S617a 등록번호 131023226 도서상태 대출가능 반납예정일 예약 서비스 B
No. 2 소장처 의학도서관/자료실(3층)/ 청구기호 001.42 S617a 등록번호 131050215 도서상태 대출가능 반납예정일 예약 서비스 B
No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 세종학술정보원/인문자료실1/ 청구기호 001.42 S617a 등록번호 151300188 도서상태 대출가능 반납예정일 예약 서비스

컨텐츠정보

목차

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

관련분야 신착자료

고려대학교. 글로벌일본연구원 (2021)
백상경제연구원 (2021)
원광대학교. 원불교사상연구원 (2021)
이희특 (2021)