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Bayesian Hierarchical Modeling with 3PNO Item Response Models (2013)
| Content Provider | CiteSeerX |
|---|---|
| Author | Sheng, Yanyan Headrick, Todd C. |
| Abstract | Fully Bayesian estimation has been developed for unidimensional IRT models. In this context, prior distributions can be specified in a hierarchical manner so that item hyperparameters are unknown and yet still have their own priors. This type of hierarchical modeling is useful in terms of the three-parameter IRT model as it reduces the difficulty of specifying model hyperparameters that lead to adequate prior distributions. Further, hierarchical modeling ameliorates the noncovergence problem associated with nonhierarchical models when appropriate prior information is not available. As such, a Fortran subroutine is provided to implement a hierarchical modeling procedure associated with the three-parameter normal ogive model for binary item response data using Gibbs sampling. Model parameters can be estimated with the choice of noninformative and conjugate prior distributions for the hyperparameters. |
| File Format | |
| Publisher Date | 2013-01-01 |
| Access Restriction | Open |
| Subject Keyword | Hierarchical Modeling Gibbs Sampling Three-parameter Normal Ogive Model Noncovergence Problem Three-parameter Irt Model Hierarchical Manner Item Response Model Bayesian Hierarchical Modeling Bayesian Estimation Model Hyperparameters Prior Distribution Conjugate Prior Distribution Unidimensional Irt Model Fortran Subroutine Item Hyperparameters Appropriate Prior Information Binary Item Response Data Model Parameter Hierarchical Modeling Procedure Nonhierarchical Model |
| Content Type | Text |
