Acceleration model in the heterogeneous case of the general graded response model

Psychometrika ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 549-572 ◽  
Author(s):  
Fumiko Samejima
Keyword(s):  
Response Model ◽  
Graded Response Model ◽  
Graded Response ◽  
Acceleration Model

IRT Information Function of Polytomously Scored Items under the Graded Response Model

2009 ◽  
Vol 40 (11) ◽  
pp. 1212-1220 ◽  
Author(s):  
Zhao-Sheng LUO ◽  
Xue-Lian OUYANG ◽  
Shu-Qing QI ◽  
Hai-Qi DAI ◽  
Shu-Liang DING
Keyword(s):  
Response Model ◽  
Graded Response Model ◽  
Information Function ◽  
Graded Response

scholarly journals The Monotonic Polynomial Graded Response Model: Implementation and a Comparative Study

2020 ◽  
Vol 44 (6) ◽  
pp. 465-481
Author(s):  
Carl F. Falk
Keyword(s):  
Latent Variable ◽  
Kernel Smoothing ◽  
Marginal Likelihood ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Response Model ◽  
Response Functions ◽  
Graded Response Model ◽  
Graded Response ◽  
Maximum Marginal Likelihood

We present a monotonic polynomial graded response (GRMP) model that subsumes the unidimensional graded response model for ordered categorical responses and results in flexible category response functions. We suggest improvements in the parameterization of the polynomial underlying similar models, expand upon an underlying response variable derivation of the model, and in lieu of an overall discrimination parameter we propose an index to aid in interpreting the strength of relationship between the latent variable and underlying item responses. In applications, the GRMP is compared to two approaches: (a) a previously developed monotonic polynomial generalized partial credit (GPCMP) model; and (b) logistic and probit variants of the heteroscedastic graded response (HGR) model that we estimate using maximum marginal likelihood with the expectation–maximization algorithm. Results suggest that the GRMP can fit real data better than the GPCMP and the probit variant of the HGR, but is slightly outperformed by the logistic HGR. Two simulation studies compared the ability of the GRMP and logistic HGR to recover category response functions. While the GRMP showed some ability to recover HGR response functions and those based on kernel smoothing, the HGR was more specific in the types of response functions it could recover. In general, the GRMP and HGR make different assumptions regarding the underlying response variables, and can result in different category response function shapes.

A note on the equivalence of the graded response model and the sequential model

Psychometrika ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 461-463 ◽  
Author(s):  
Timo M. Bechger ◽  
Wies Akkermans
Keyword(s):  
Response Model ◽  
Graded Response Model ◽  
Sequential Model ◽  
Graded Response

scholarly journals A Minimum χ2 Method for Equating Tests Under the Graded Response Model

1995 ◽  
Vol 19 (2) ◽  
pp. 167-176 ◽  
Author(s):  
Seock-Ho Kim ◽  
Allan S. Cohen
Keyword(s):  
Response Model ◽  
Graded Response Model ◽  
Graded Response
Sign in / Sign up

Export Citation Format

Share Document