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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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4 a7 u! q6 t; a2 e& }Title: Age-period-cohort analysis: new models, methods, and empirical applications. s. ^/ Y4 b$ B5 ^& Y+ t" |7 M- g1 ]
Author(s): Yang Yang; Kenneth C Land
$ @6 i6 }% C# Q) c. BSeries: Interdisciplinary statistics
7 p+ b% V5 `2 C' R# s5 n" jPublisher: CRC Press        9 r# Q) f7 I# ~6 k
City: Boca Raton, FL5 [1 u; H* `2 N- J2 t
Year: 2013+ ?% l/ Y0 W* Y
Language: English        8 M  Q0 L- f) ^2 x& J6 Q3 R1 h
Pages: 3385 }5 |3 ?8 x0 w6 L
ISBN: 9781466507524, 1466507527% F, Y3 D0 Z* c  e
Size: 4 MB (4116337 bytes)        ; E1 {8 U/ {" M- T0 a7 m: {
Extension: pdf
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" ]6 d+ S5 A7 d% ISummary
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Age-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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% f5 f$ \1 Q% Y) r& XThe 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.
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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
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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.
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! d$ ?5 [. O& y5 y9 L+ }+ UKenneth 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.; X  y) M( K; v* [

) @; J! D5 W8 m( W, ZTable of Contents
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5 R/ h0 A% R9 }, S- p) eIntroduction0 ^+ o# `0 U3 X
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Why Cohort Analysis?
# T6 r1 `( Z* L4 t6 k8 lIntroduction
" g# v* b, @! [/ y) IThe Conceptualization of Cohort Effects6 b! s. F* q/ ]4 v1 _) g+ ]2 Y+ I
Distinguishing Age, Period, and Cohort
' v# g9 z# q# L( a6 e+ B' QSummary
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& ]* S0 r& z; p" e& jAPC Analysis of Data from Three Common Research Designs
7 [+ @; s1 V: R) DIntroduction4 @! R# R6 L7 {0 E. O
Repeated Cross-Sectional Data Designs2 P. K! O1 l. }
Research Design I: Age-by-Time Period Tabular Array of Rates/Proportions& {8 _7 g& I. G8 ^
Research Design II: Repeated Cross-Sectional Sample Surveys
. R7 v: I# l$ S% K( q4 z0 _Research Design III: Prospective Cohort Panels and the Accelerated Longitudinal Design
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Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework1 U0 b3 L$ m; I
Introduction0 N. j" N/ _7 A/ f/ k
Descriptive APC Analysis
5 \# j9 X% |7 p, n2 `. Z8 YAlgebra of the APC Model Identification Problem
1 f* E$ q  z% M/ |Conventional Approaches to the APC Identification Problem! l2 `" s0 A( ]* \$ y+ ]1 {& g
Generalized Linear Mixed Models (GLMM) Framework1 h; D. w" W2 [$ [/ k4 Y) u9 S- D
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APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator" z. k+ D, w7 P! h; r
Introduction6 z$ v3 o8 G' f5 ^9 k
Algebraic, Geometric, and Verbal Definitions of the Intrinsic Estimator
7 N: c/ o) Y& N2 K0 A' dStatistical Properties% \$ w# C0 \% [8 k% V; g! p+ M
Model Validation: Empirical Example" [" d1 p  w) p" X2 Q- q
Model Validation: Monte Carlo Simulation Analyses
2 r; w) k! |4 [+ G5 S" c% eInterpretation and Use of the Intrinsic Estimator" u- u' V. A5 [9 c
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APC Accounting/Multiple Classification Model, Part II: Empirical Applications6 ~1 G, F+ E# N5 H7 w4 h  x8 c
Introduction( v/ i0 |8 E, z9 G2 _; T
Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure. N% S( _& A! [+ ?/ W' J2 A
APC Model-Based Demographic Projection and Forecasting
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Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics
$ k6 f* B+ v! D; E8 G( }& s& |Introduction
4 m1 Z5 C6 [/ \/ h! J: H  fBeyond the Identification Problem
" O3 P$ H. X4 Y( ]Basic Model Specification& \  M" ]7 K; D" ^- @
Fixed versus Random Effects HAPC Specifications
" k" q1 d7 c2 w) R$ Z6 \8 M# E; f$ P( KInterpretation of Model Estimates3 |% {* P0 T0 @
Assessing the Significance of Random Period and Cohort Effects. }4 r/ |  c5 z+ V5 _6 y+ m9 x2 c
Random Coefficients HAPC-CCREM
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- A6 I$ I  t8 v8 BMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses
9 Y2 H7 {$ }9 m  v- X6 YIntroduction
( F& C) E9 K% _; I2 LLevel 2 Covariates: Age and Temporal Changes in Social Inequalities in Happiness2 x2 ]5 M. s* H7 n
HAPC-CCREM Analysis of Aggregate Rate Data on Cancer Incidence and Mortality
4 F2 B# @: Y( K' o$ AFull Bayesian Estimation" D4 G4 c5 v9 ]( g. I. q
HAPC-Variance Function Regression$ w% j1 ^9 N  ?4 S2 ?' o& Y
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Mixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data0 P6 U' l2 B. L' }7 E
Introduction
1 V! d* I/ U6 P- D  ^Intercohort Variations in Age Trajectories
' W, f- R5 @* E" N! \# g# Y; zIntracohort Heterogeneity in Age Trajectories
5 ~" `( Q3 V' B7 B8 yIntercohort Variations in Intracohort Heterogeneity Patterns
( Z* T2 h; T5 Y. t" h2 p2 tSummary
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3 I2 n* g2 [. ?; X' wDirections for Future Research and Conclusion/ K5 ?5 p5 ]3 x
Introduction
; x) r7 W/ X% y& r+ rAdditional Models
$ k, W% s( o( F9 _: J/ X  kLongitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories
9 c6 y- L* b) D5 ^, ?Conclusion
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Index
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Age-Period-Cohort Analysis New Models, Methods, and Empirical Applications (2013).pdf (3.93 MB, 下载次数: 77)

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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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