This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for 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. They 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 existence of the present volume can be traced to methodological concerns about cohort analysis, all of which were evident throughout most of the social sciences by the late 1970s. For some social scientists, they became part of a broader discussion concerning the need for new analytical techniques for research based on longitudinal data. In 1976, the Social Science Research Council (SSRC), with funds from the National Institute of Education, established a Committee on the Methodology of Longitudinal Research. (The scholars who comprised this committee are listed at the front of this volume. ) As part of the efforts of this Committee, an interdisciplinary conference on cohort analysis was held in the summer of 1979, in Snowmass, Colorado. Much of the work presented here stems from that conference, the purpose of which was to promote the development of general methodological tools for the study of social change. The conference included five major presentations by (1) William Mason and Herbert Smith, (2) Karl J6reskog and Dag S6rbom, (3) Gregory Markus, (4) John Hobcraft, Jane Menken and Samuel Preston, and (5) Stephen Fienberg and William Mason. The formal presentations were each followed by extensive discussion, which involved as participants: Paul Baltes, William Butz, Philip Converse, Otis Dudley Duncan, David Freedman, William Meredith, John Nesselroade, Daniel Price, Thomas Pullum, Peter Read, Matilda White Riley, Norman Ryder, Warren Sanderson, Warner Schaie, Burton Singer, Nancy Tuma, Harrison White, and Halliman Winsborough.

A method for studying changes in group patterns -- particularly groups based on age -- cohort analysis seeks to isolate changes attributable to alterations in behaviour or attitudes within an age group; as an example of behaviour change, the pattern of consumption of alcohol within a cohort is analyzed.

"This book is key reference material for researchers wanting to know how to appropriately deal with Age-Period-Cohort issues in their statistical modelling. It deals with the identification problem of working with co-linear variables, why some currently used methods are problematic, and suggests ideas for what applied researchers interested in APC analysis should do. Suitable for all those working with APC effects in human data, the book is particularly suitable for social scientists with a moderate level of quantitative understanding"--

This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for 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. They 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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Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently investment, healthcare and pension contribution in economics and finance. Although APC analysis has been studied for the past 40 years and a lot of methods have been developed, the identification problem has been a major hurdle in analyzing APC data, where the regression model has multiple estimators, leading to indetermination of parameters and temporal trends. A Practical Guide to Age-Period Cohort Analysis: The Identification Problem and Beyond provides practitioners a guide to using APC models as well as offers graduate students and researchers an overview of the current methods for APC analysis while clarifying the confusion of the identification problem by explaining why some methods address the problem well while others do not. Features · Gives a comprehensive and in-depth review of models and methods in APC analysis. · Provides an in-depth explanation of the identification problem and statistical approaches to addressing the problem and clarifying the confusion. · Utilizes real data sets to illustrate different data issues that have not been addressed in the literature, including unequal intervals in age and period groups, etc. Contains step-by-step modeling instruction and R programs to demonstrate how to conduct APC analysis and how to conduct prediction for the future Reflects the most recent development in APC modeling and analysis including the intrinsic estimator Wenjiang Fu is a professor of statistics at the University of Houston. Professor Fu’s research interests include modeling big data, applied statistics research in health and human genome studies, and analysis of complex economic and social science data.