skip to main content skip to footer

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

Author(s):
Sinharay, Sandip; Johnson, Matthew.; Williamson, David M.
Publication Year:
2003
Report Number:
RR-03-04
Source:
ETS Research Report
Document Type:
Report
Page Count:
41
Subject/Key Words:
Bayesian Hierarchical Model, Markov Chain Monte Carlo (MCMC), Item Model, Automatic Item Generation (AIG), Expected Response Function, Bayesian Methods

Abstract

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.

Read More