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

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.

Bayesian estimation of phase response curves

2010 ◽  
Vol 23 (6) ◽  
pp. 752-763 ◽  
Author(s):  
Ken Nakae ◽  
Yukito Iba ◽  
Yasuhiro Tsubo ◽  
Tomoki Fukai ◽  
Toshio Aoyagi
Keyword(s):  
Bayesian Estimation ◽  
Phase Response ◽  
Response Curves ◽  
Phase Response Curves
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