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Age-Period-Cohort Models

Approaches and Analyses with Aggregate Data

Develop a Deep Understanding of the Statistical Issues of APC Analysis


Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. Les mer
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Legg i
Vår pris: 1096,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

Develop a Deep Understanding of the Statistical Issues of APC Analysis


Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data 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.


See How Common Methods Are Related to Each Other





After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects.


Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables





This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.

Fakta

Innholdsfortegnelse

Introduction to the Age, Period, and Cohort Mix
Introduction
Interest in Age, Period, and Cohort
Importance of Cohorts
Plan for the Book





Multiple Classification Models and Constrained Regression
Introduction
Linearly Coded Age-Period-Cohort (APC) Model
Categorically Coded APC Model
Generalized Linear Models
Null Vector
Model Fit
Solution Is Orthogonal to the Constraint
Examining the Relationship between Solutions
Differences between Constrained Solutions as Rotations of Solutions
Solutions Ignoring One or More of the Age, Period, or Cohort Factors
Bias: Constrained Estimates and the Data Generating Parameters
Unbiased Estimation under a Constraint
A Plausible Constraint with Some Extra Empirical Support





Geometry of APC Models and Constrained Estimation
Introduction
General Geometric View of Rank Deficient by One Models
Generalization to Systems with More Dimensions
APC Model with Linearly Coded Variables
Equivalence of the Geometric and Algebraic Solutions
Geometry of the Multiple Classification Model
Distance from Origin and Distance along the Line of Solutions
Empirical Example: Frost's Tuberculosis Data
Summarizing Some Important Features from the Geometry of APC Models
Problem with Mechanical Constraints





Estimable Functions Approach
Introduction
Estimable Functions
l'sv Approach for Establishing Estimable Functions in APC Models
Some Examples of Estimable Functions Derived Using the l'sv Approach
Comments on the l'sv Approach
Estimable Functions with Empirical Data
More Substantive Examination of Differences of Male and Female Lung Cancer Mortality Rates





Partitioning the Variance in APC Models
Introduction
Age-Period-Cohort Analysis of Variance (APC ANOVA) Approach to Attributing Variance
APC Mixed Model
Hierarchical APC Model
Empirical Example Using Homicide Offending Data





Factor-Characteristic Approach
Introduction
Characteristics for One Factor
Characteristics for Two or More Factors
Variance Decomposition for Factors and for Factor Characteristics
Empirical Examples: Age-Period-Specific Suicide Rates and Frequencies
Age-Period-Cohort Characteristics (APCC) Analysis of Suicide Data with Two Cohort Characteristics
Age-Cohort-Period Characteristics (ACPC) Analysis of the Suicide Data with Two Period Characteristics
Age-Period-Characteristics-Cohort Characteristics Model
Approaches Based on Factor Characteristics and Mechanism
Additional Features and Analyses of Factor-Characteristic Models





Conclusions: An Empirical Example
Introduction
Empirical Example: Homicide Offending





Index





Conclusions and References appear at the end of each chapter.

Om forfatteren

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.