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Bayesian Hierarchical Response Modeling

  • Jean-Paul Fox
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

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.

Keywords

Prior Distribution Success Probability Item Parameter Prior Density Inverse Gamma 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Jean-Paul Fox
    • 1
  1. 1.Department of Research Methodology, Measurement, and Data Analysis Faculty of Behavioral SciencesUniversity of TwenteEnschedeThe Netherlands

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