期刊
INTERNATIONAL JOURNAL OF RESEARCH & METHOD IN EDUCATION
卷 32, 期 1, 页码 103-125出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/17437270902749353
关键词
Bayesian inference; mean and covariance structure analysis; missing values; data-dependent priors
资金
- office of the John A. Hannah Chair in the College of Education, Michigan State University
The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection. As a simplified demonstration, we illustrate (1) how Bayesians test and compare two non-nested growth curve models using Bayesian estimation with non-informative prior; (2) how Bayesians model and handle missing outcomes in the context of missing values; and (3) how Bayesians incorporate data-based evidence from a previous data set, construct informative priors and treat them as extra information while conducting an up-to-date analogy analysis.
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