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Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling

Bowling Green State University

The problem of estimating item parameters from a two-parameter normal ogive model is considered. Gibbs sampling (Gelfand & Smith, 1990) is used to simulate draws from the joint posterior distribution of the ability and item parameters. This method gives marginal posterior density estimates for any parameter of interest; these density estimates can be used to judge the accuracy of normal approximations based on maximum likelihood estimates. This simulation technique is illustrated using data from a mathematics placement exam.

Key Words: density estimates • EM algorithm • simulation

Journal of Educational and Behavioral Statistics, Vol. 17, No. 3, 251-269 (1992)
DOI: 10.3102/10769986017003251


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