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[分享] Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications

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sampson2010 发表于 2015-3-19 09:09:25 | 显示全部楼层 |阅读模式

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. d& J4 Q9 t9 @. R1 iTitle: Age-period-cohort analysis: new models, methods, and empirical applications8 U9 \* f# g& v3 B5 d; ]
Author(s): Yang Yang; Kenneth C Land
4 N7 z. X$ O) GSeries: Interdisciplinary statistics7 r: ^4 M- [' {2 L1 s
Publisher: CRC Press       
# V8 M) _% E/ }" d& ^5 kCity: Boca Raton, FL
0 T' `, F2 {) t% G8 s5 X! L' oYear: 2013) O* x* f5 Q/ m2 F0 m0 H" v
Language: English       
8 t1 |+ G! @/ y2 E$ u3 cPages: 338" Q( {3 G. N1 F$ J- H0 V
ISBN: 9781466507524, 1466507527
2 [9 V; B3 Y; e' G0 r9 ISize: 4 MB (4116337 bytes)       
0 y. ?7 v. ~; @* W1 h( O4 xExtension: pdf
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Summary
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5 M; L  z. D: N- ?, m6 c7 eAge-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors’ collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.
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The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends.2 y/ @6 \$ n2 u7 \6 J* w
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Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book’s web page.
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Author(s) Bio. H/ b9 o+ ^- V, A: p# j/ y

3 q6 O1 `# q3 }% [% d3 A/ {Yang Yang is an associate professor in the Department of Sociology and Lineberger Comprehensive Cancer Center and a faculty fellow in the Carolina Population Center at the University of North Carolina-Chapel Hill. Dr. Yang’s research encompasses the areas of demography, medical sociology, cancer, and quantitative methodology. Her work has been featured in numerous media outlets, including the American Sociological Review, CNN, Associated Press, Reuters, Washington Post, and Chicago Tribune. She received a Ph.D. in sociology from Duke University.9 a# ^- Q0 s% {
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Kenneth C. Land is a John Franklin Crowell professor of sociology and faculty director of the Center for Population Health and Aging at Duke University. Dr. Land is a fellow of the American Statistical Association, the Sociological Research Association, the American Association for the Advancement of Science, the International Society for Quality-of-Life Studies, and the American Society of Criminology. His research focuses on contemporary social trends and quality-of-life measurement, social problems, demography, criminology, organizations, and mathematical and statistical models and methods for the study of social and demographic processes. He received a Ph.D. in sociology and mathematics from the University of Texas at Austin.+ l' T: m+ p% i
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Table of Contents
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5 a6 c( j  U  [" AIntroduction0 g% M% `! U0 a5 o7 H. i- J

2 }7 {  `2 ^( q3 q% h/ |Why Cohort Analysis?
. x3 |! c7 H  v* P7 I* vIntroduction0 [2 {& h/ U6 J
The Conceptualization of Cohort Effects
! p, ?( N8 t$ G' k3 d- I" d* U$ gDistinguishing Age, Period, and Cohort
% a7 e' s& o* TSummary
, y" i& g9 Y2 t( V9 Q' [- H/ Q0 s6 |/ @, A8 }4 @9 y5 H
APC Analysis of Data from Three Common Research Designs/ z, g2 m: G0 @) @0 v
Introduction( W% h5 U3 c  u
Repeated Cross-Sectional Data Designs
% f/ X2 m( g% X$ @, s: lResearch Design I: Age-by-Time Period Tabular Array of Rates/Proportions
* d! F, W* h! M$ L3 l) F, UResearch Design II: Repeated Cross-Sectional Sample Surveys$ p/ S9 t! d; H* P. N4 u
Research Design III: Prospective Cohort Panels and the Accelerated Longitudinal Design
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8 E8 {! H0 Q, `, sFormalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework. N# P) c6 h) w' F  I
Introduction
: F8 T( {3 A. Y5 o6 WDescriptive APC Analysis; z; {/ {. H" Q% }5 U) @
Algebra of the APC Model Identification Problem
) f5 [' g& Y1 l+ f# M- v/ rConventional Approaches to the APC Identification Problem; S4 X( u- O8 k5 {$ R
Generalized Linear Mixed Models (GLMM) Framework
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APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator8 U0 C9 J) `2 D2 f
Introduction& I! C& M( s* Y! B8 a5 [
Algebraic, Geometric, and Verbal Definitions of the Intrinsic Estimator" c; M8 T- V' h' y
Statistical Properties0 v0 ^, Q$ a* H6 O
Model Validation: Empirical Example. x% ~, b0 i6 ~1 J3 ^, _
Model Validation: Monte Carlo Simulation Analyses( Z' B9 a2 G- Y' d
Interpretation and Use of the Intrinsic Estimator
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. h# w( o/ f- s7 b- B. }5 ]; ^, `APC Accounting/Multiple Classification Model, Part II: Empirical Applications8 t3 G% a* k( h' s7 J
Introduction$ y& T7 T/ C! K$ n4 K
Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure% j( e( W% n- _( D
APC Model-Based Demographic Projection and Forecasting6 Y: P7 @' u, e4 o

5 c* V* g9 Y0 H& G4 I  ~Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics
2 d! Y% i& ~+ z4 R# Q$ ?9 M  e2 u1 J. [Introduction, K/ R6 r- E+ u
Beyond the Identification Problem8 ]0 N) b1 E8 B4 C
Basic Model Specification6 K' n3 B/ p% `  |+ K& j
Fixed versus Random Effects HAPC Specifications
& T6 i1 |4 p1 K! PInterpretation of Model Estimates
$ ?8 r" R% ^. S# J- c2 P/ o) aAssessing the Significance of Random Period and Cohort Effects0 O. t* x3 ^6 O% {" G) q$ r! s7 [$ n
Random Coefficients HAPC-CCREM
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% A9 S6 p6 x' g" YMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses/ Q- ^( z' L. z" C9 G2 W
Introduction
, Y6 n. `6 ?& z/ FLevel 2 Covariates: Age and Temporal Changes in Social Inequalities in Happiness) W$ N9 u3 K  i! F1 k* [, Y
HAPC-CCREM Analysis of Aggregate Rate Data on Cancer Incidence and Mortality% W. g) {: q; l! i8 I; l" K# P  n
Full Bayesian Estimation
8 z/ p4 S6 r, o4 v: \2 k9 THAPC-Variance Function Regression* J0 N; ~' Z; E" L, d7 n
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Mixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data
+ g4 l" {, P6 N: v( Q1 o+ }Introduction
, u/ w' a( c  O; c% UIntercohort Variations in Age Trajectories5 h! F5 s: w$ ~# z: U0 ?7 A7 O' O  u
Intracohort Heterogeneity in Age Trajectories
7 x9 J. U9 Z" v* N$ y& fIntercohort Variations in Intracohort Heterogeneity Patterns
( W! g) @! @& B6 q+ H9 USummary' H! [" t# v( d

! L/ S- L2 s& ~" ADirections for Future Research and Conclusion. W- o* \% o: C" z$ j( p
Introduction
. z: Z% y' m* L( i+ OAdditional Models$ r: D3 f: K  x. C0 X( r0 o3 p% Z
Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories
% a# P/ r1 m% Y8 `) t5 [9 Y% l# WConclusion
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* ?, X. L2 s* ]1 F) ~, K) EIndex7 c* S1 F" N: z! Y; P3 h

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Age-Period-Cohort Analysis New Models, Methods, and Empirical Applications (2013).pdf (3.93 MB, 下载次数: 78)

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yuyuzhazha 发表于 2018-9-10 11:06:11 | 显示全部楼层
最近在看APC, 收了学习,感谢
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zhek 发表于 2018-9-14 09:35:31 | 显示全部楼层
这是个好材料,活学活用。
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