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

Overview

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...

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Overview

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.

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Editorial Reviews

From the Publisher
"This book achieves its intended contributions to quantitative studies of time-related change. … This book presents all the important background and new developments in one place, with examples and software for easy applications. I would recommend this book to anyone working in various research disciplines that rely on APC analysis."
Journal of the American Statistical Association, June 2014
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Product Details

Meet 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.

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Table of Contents

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.

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