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- Title
BAYESIAN INFERENCE FOR HIERARCHICAL AGE-PERIOD-COHORT MODELS OF REPEATED CROSS-SECTION SURVEY DATA.
- Authors
Yang, Yang
- Abstract
This study applies methods of Bayesian statistical inference to hierarchical APC models for the age-period-cohort analysis of repeated cross-section survey data. It examines the impacts of small sample sizes of birth cohorts and time periods and unbalanced data on statistical inferences based on the usual restricted maximum likelihood–empirical Bayes (REML-EB) estimators through Monte Carlo simulations. A full Bayesian analysis using Gibbs sampling and MCMC estimation is developed to assess the robustness of REML-EB inferences when this extra uncertainty is taken into account and the numbers of higher-level units are small. For a substantive illustration, it applies cross-classified random effects models to vocabulary test data from the General Social Survey (1974 to 2000). It is concluded that the decline in verbal ability for birth cohorts born after 1950 was correlated with the levels of newspaper reading and television watching. Avenues for future research on mixed APC models are discussed. INSETS: Table 1: Summary Statistics for Verbal Ability Data from the GSS;Graph 1: Cohort Characteristics;Table 2: Alternative Estimates of Coefficients and Summary Stats;Distribution of Cohort Members
- Subjects
BAYESIAN analysis; VERBAL ability; NEWSPAPER reading; TELEVISION viewers -- Social aspects; DEMOGRAPHY -- Social aspects; MONTE Carlo method; COHORT analysis; SOCIAL change; SOCIAL development; SOCIOLOGY methodology
- Publication
Sociological Methodology, 2006, Vol 36, Issue 1, p39
- ISSN
0081-1750
- Publication type
Article
- DOI
10.1111/j.1467-9531.2006.00174.x