scholarly journals Use of item response curves of the Force and Motion Conceptual Evaluation to compare Japanese and American students’ views on force and motion

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Michi Ishimoto ◽  
Glen Davenport ◽  
Michael C. Wittmann
Keyword(s):  
Item Response ◽  
Response Curves ◽  
Force And Motion ◽  
American Students

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 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

Estimation of Two-Parameter Logistic Item Response Curves

1984 ◽  
Vol 9 (4) ◽  
pp. 263-276 ◽  
Author(s):  
Robert K. Tsutakawa
Keyword(s):  
Maximum Likelihood ◽  
Item Response ◽  
Information Matrix ◽  
Simulated Data ◽  
Maximum Likelihood Estimates ◽  
Response Curves ◽  
Numerical Work ◽  
Item Parameters ◽  
The Em Algorithm ◽  
Two Parameter

Under the assumption that ability parameters are sampled from a normal distribution, the EM algorithm is used to derive maximum likelihood estimates for item parameters of the two-parameter logistic item response curves. The observed information matrix is then used to approximate the covariance matrix of these estimates. Responses to a questionnaire on general arthritis knowledge are used to illustrate the procedure and simulated data are used to compare the estimated and actual item parameters. The resulting estimates are found to be very close to those obtained from LOGIST. A computational note is included to facilitate the extensive numerical work required to implement the procedure.

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