Age-period-cohort analysis : new models, methods, and empirical applications

Yang Yang and Kenneth C. Land

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. 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. 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.

「Nielsen BookData」より


  • Introduction Why Cohort Analysis? Introduction The Conceptualization of Cohort Effects Distinguishing Age, Period, and Cohort Summary APC Analysis of Data from Three Common Research Designs Introduction Repeated Cross-Sectional Data Designs Research Design I: Age-by-Time Period Tabular Array of Rates/Proportions Research Design II: Repeated Cross-Sectional Sample Surveys Research Design III: Prospective Cohort Panels and the Accelerated Longitudinal Design Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework Introduction Descriptive APC Analysis Algebra of the APC Model Identification Problem Conventional Approaches to the APC Identification Problem Generalized Linear Mixed Models (GLMM) Framework APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator Introduction Algebraic, Geometric, and Verbal Definitions of the Intrinsic Estimator Statistical Properties Model Validation: Empirical Example Model Validation: Monte Carlo Simulation Analyses Interpretation and Use of the Intrinsic Estimator APC Accounting/Multiple Classification Model, Part II: Empirical Applications Introduction Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure APC Model-Based Demographic Projection and Forecasting Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics Introduction Beyond the Identification Problem Basic Model Specification Fixed versus Random Effects HAPC Specifications Interpretation of Model Estimates Assessing the Significance of Random Period and Cohort Effects Random Coefficients HAPC-CCREM Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses Introduction Level 2 Covariates: Age and Temporal Changes in Social Inequalities in Happiness HAPC-CCREM Analysis of Aggregate Rate Data on Cancer Incidence and Mortality Full Bayesian Estimation HAPC-Variance Function Regression Mixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data Introduction Intercohort Variations in Age Trajectories Intracohort Heterogeneity in Age Trajectories Intercohort Variations in Intracohort Heterogeneity Patterns Summary Directions for Future Research and Conclusion Introduction Additional Models Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories Conclusion Index References appear at the end of each chapter.

「Nielsen BookData」より


書名 Age-period-cohort analysis : new models, methods, and empirical applications
著作者等 Land, Kenneth C.
Yang, Yang
シリーズ名 Interdisciplinary statistics
出版元 CRC Press
刊行年月 c2013
ページ数 xiii, 338 p.
大きさ 24 cm
ISBN 9781466507524
NCID BB12901851
※クリックでCiNii Booksを表示
言語 英語
出版国 アメリカ合衆国