JSM 2004 - Toronto

Abstract #300827

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Activity Number: 216
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #300827
Title: A Bayesian Hierarchical Modeling Approach to Age-period-cohort Analyses of Repeated Cross-section Survey Data
Author(s): Yang Yang*+
Companies: Duke University
Address: Dept. of Sociology and Institute of Statistics and Decision Sciences, Durham, NC, 27708,
Keywords: age-period-cohort analysis ; repeated cross-sections ; hierarchical model ; cross-classified random effects model ; restricted maximum likelihood ; Bayesian inference
Abstract:

It is well known that the identification problem created by the exact linear dependency of age (A), period (P), and cohort © presents a challenge to conventional age-period-cohort (APC) analysis in demography and social science research. This study developed a new approach, namely, Bayesian hierarchical APC models of micro datasets in the form of repeated cross-section survey designs. It examined the impact of small sample sizes of birth cohorts and time periods and unbalanced data on statistical inference based on the usual REML-EB estimation through Monte Carlo simulations. A full Bayesian analysis using Gibbs sampling and MCMC estimation was conducted to strengthen the inference by accounting for this extra uncertainty associated with parameter estimates when the numbers of higher-level units are small. For a substantive illustration, it applied cross-classified random effects models to the vocabulary data from the General Social Survey from 1974 to 2000. The results shed new light on the controversy on APC trends in verbal ability in the U.S. for the past three decades.

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