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An Application of a Bayesian Hierarchical Model for Item Family Calibration MCMC AIG

Sinharay, Sandip; Johnson, Matthew.; Williamson, David M.
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
Bayesian Hierarchical Model, Markov Chain Monte Carlo (MCMC), Item Model, Automatic Item Generation (AIG), Expected Response Function, Bayesian Methods


improvement over the models current used in similar situations. In the present report, the authors introduce the notion of the family expect response function (FERF) as a way to summarize the probability of a correct response to an item randomly generated from an item family and suggest a way to estimate FERFs. This work is a step towards creating a tool that can save significant amounts of resources for tests with item families, because calibrating only the item families might be enough rather than calibrating each item in the facilities separately.

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