Bayesian Hierarchical Response Modeling
In the _rst chapter, an introduction to Bayesian item response modeling was given. The Bayesian methodology requires careful speci_cation of priors since item response models contain many parameters, often of the same type. A hierarchical modeling approach is introduced that supports the pooling of information to improve the precision of the parameter estimates. The Bayesian approach for handling response modeling issues is given, and speci_c Bayesian elements related to response modeling problems will be emphasized. It will be shown that the Bayesian paradigm engenders new ways of dealing with measurement error, limited information about many individuals, clustered response data, and di_erent sources of information.
KeywordsPrior Distribution Success Probability Item Parameter Prior Density Inverse Gamma
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