Exploiting Auxiliary Information About Items in the Estimation of Rasch Item Difficulty Parameters

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Abstract
Standard procedures for estimating the item param eters in IRT models make no use of auxiliary informa tion about test items, such as their format, their con tent, or the skills they require for solution. This paper describes a framework for exploiting this information, thereby enhancing the precision and stability of item parameter estimates and providing diagnostic informa tion about items' operating characteristics. The princi ples are illustrated in a context for which a relatively simple approximation is available: empirical Bayesian estimation of Rasch item difficulty parameters. Index terms: Bayesian estimation, Collateral informa tion, Empirical Bayesian estimation, Exchangeability, Hierarchical models, Item response theory, Linear lo gistic test model, Rasch model item parameters.