Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling
- 1 September 1992
- journal article
- Published by American Educational Research Association (AERA) in Journal of Educational Statistics
- Vol. 17 (3) , 251-269
- https://doi.org/10.3102/10769986017003251
Abstract
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.Keywords
This publication has 15 references indexed in Scilit:
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Bayesian Estimation of Item Response CurvesPsychometrika, 1986
- Estimation of Two-Parameter Logistic Item Response CurvesJournal of Educational Statistics, 1984
- Bayesian analysis of dichotomous quantal response modelsJournal of Econometrics, 1984
- Estimation of Two-Parameter Logistic Item Response CurvesJournal of Educational Statistics, 1984
- Bayesian Estimation in the Rasch ModelJournal of Educational Statistics, 1982
- Bayesian Estimation in the Rasch ModelJournal of Educational Statistics, 1982
- Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM AlgorithmPsychometrika, 1981
- Fitting a Response Model for n Dichotomously Scored ItemsPsychometrika, 1970
- An Application of Confidence Intervals and of Maximum Likelihood to the Estimation of an Examinee's AbilityPsychometrika, 1953