Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling

1992 ◽  
Vol 17 (3) ◽  
pp. 251 ◽  
Author(s):  
James H. Albert
Keyword(s):  
Bayesian Estimation ◽  
Item Response ◽  
Gibbs Sampling ◽  
Response Curves

Bayesian estimation of item response curves

Psychometrika ◽  
1986 ◽  
Vol 51 (2) ◽  
pp. 251-267 ◽  
Author(s):  
Robert K. Tsutakawa ◽  
Hsin Ying Lin
Keyword(s):  
Bayesian Estimation ◽  
Item Response ◽  
Response Curves

Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling

1992 ◽  
Vol 17 (3) ◽  
pp. 251-269 ◽  
Author(s):  
James H. Albert
Keyword(s):  
Gibbs Sampling ◽  
Maximum Likelihood Estimates ◽  
Posterior Density ◽  
Response Curves ◽  
Density Estimates ◽  
Mathematics Placement ◽  
Item Parameters ◽  
Normal Ogive Model ◽  
Using Data ◽  
Placement Exam

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.

scholarly journals The algorithm of Bayesian estimation of maternal genetic and permanent maternal environmental variances in a two-trait binary threshold model

2011 ◽  
Vol 49 (No. 2) ◽  
pp. 58-63
Author(s):  
E. Skotarczak ◽  
M. Szyd ◽  
A. Dobek ◽  
K. Moli ◽  
T. Szwaczkowski
Keyword(s):  
Bayesian Estimation ◽  
Gibbs Sampling ◽  
Random Effects ◽  
Environmental Effects ◽  
Fixed Effects ◽  
Threshold Model ◽  
Sampling Procedure ◽  
Numerical Example ◽  
Estimation And Prediction

The paper presents an algorithm for the estimation and prediction of parameters in a two-trait binary threshold model. The model includes fixed effects and the following random effects: genetic direct additive, genetic maternal additive and permanent maternal environmental effects. The Gibbs sampling procedure was used to estimate the parameters. The algorithm was illustrated with a numerical example showing appropriateness of the proposed method.  

Nonparametric Item Response Curve Estimation With Correction for Measurement Error

2011 ◽  
Vol 36 (6) ◽  
pp. 755-778 ◽  
Author(s):  
Hongwen Guo ◽  
Sandip Sinharay
Keyword(s):  
Measurement Error ◽  
Item Response ◽  
Response Curve ◽  
Item Analysis ◽  
Kernel Estimation ◽  
Regression Estimation ◽  
Response Curves ◽  
Curve Estimation ◽  
Kernel Regression Estimation ◽  
Operational Data

Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally. This study investigates the deconvolution kernel estimation of IRCs, which corrects for the measurement error in the regressor variable. A comparison of the traditional kernel estimation and the deconvolution estimation of IRCs is carried out using both simulated and operational data. It is found that, in item analysis, the traditional kernel estimation is comparable to the deconvolution kernel estimation in capturing important features of the IRC.

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