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Age-Period-Cohort Analysis


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Table of Contents


Why Cohort Analysis?
The Conceptualization of Cohort Effects
Distinguishing Age, Period, and Cohort
SummaryAPC Analysis of Data from Three Common Research Designs
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 DesignFormalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework
Descriptive APC Analysis
Algebra of the APC Model Identification Problem
Conventional Approaches to the APC Identification Problem
Generalized Linear Mixed Models (GLMM) FrameworkAPC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator
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 EstimatorAPC Accounting/Multiple Classification Model, Part II: Empirical Applications
Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure
APC Model-Based Demographic Projection and ForecastingMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics
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-CCREMMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses
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 RegressionMixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data
Intercohort Variations in Age Trajectories
Intracohort Heterogeneity in Age Trajectories
Intercohort Variations in Intracohort Heterogeneity Patterns
SummaryDirections for Future Research and Conclusion
Additional Models
Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories
ConclusionIndex References appear at the end of each chapter.

About the Author

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. 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.


" ... presents all the important background and new developments in one place, with examples and software for easy applications. I would recommendthis book to anyone working in various research disciplines that rely on APC analysis."
-Journal of the American Statistical Association

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