This book presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. Emphasizing both the geometry and algebra of several APC approaches, the book develops readers' understanding of the statistical issues of APC analysis and shows how common methods are related to each other.
This book presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. Emphasizing both the geometry and algebra of several APC approaches, the book develops readers' understanding of the statistical issues of APC analysis and shows how common methods are related to each other.
Robert M. O'Brien is a professor emeritus of sociology at the University of Oregon. He specializes in criminology and quantitative methods and has published extensively in both areas. In what is labeled semiretirement, he coedits Sociological Perspectives with James R. Elliott and Jean Stockard and works on research projects.
Inhaltsangabe
Introduction to the Age, Period, and Cohort Mix. Multiple Classification Models and Constrained Regression. Geometry of APC Models and Constrained Estimation. Estimable Functions Approach. Partitioning the Variance in APC Models. Factor-Characteristic Approach. Conclusions: An Empirical Example. Index.
Introduction to the Age, Period, and Cohort Mix. Multiple Classification Models and Constrained Regression. Geometry of APC Models and Constrained Estimation. Estimable Functions Approach. Partitioning the Variance in APC Models. Factor-Characteristic Approach. Conclusions: An Empirical Example. Index.
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