期刊
SOCIOLOGICAL METHODS & RESEARCH
卷 32, 期 3, 页码 301-335出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0049124103257303
关键词
Bayesian posterior predictive distributions; Bayesian approach; statistics; model fit
In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specifically, as well as a Bayesian approach to modeling snore generally, in tackling these issues. First, they review the Bayesian approach to statistics and computation. Second, they discuss the evaluation of model fit in a bivariate probit model. Third, they discuss comparing fixed- and random-effects hierarchical linear models. Both examples highlight the use of Bayesian posterior predictive distributions beyond these particular cases.
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