Computerized Classification Testing Under the Generalized Graded Unfolding Model

2011 ◽  
Vol 71 (1) ◽  
pp. 114-128 ◽  
Author(s):  
Wen-Chung Wang ◽  
Chen-Wei Liu
Keyword(s):  
Unfolding Model ◽  
Generalized Graded Unfolding Model

The Explanatory Generalized Graded Unfolding Model: Incorporating Collateral Information to Improve the Latent Trait Estimation Accuracy

2021 ◽  
pp. 014662162110517
Author(s):  
Seang-Hwane Joo ◽  
Philseok Lee ◽  
Stephen Stark
Keyword(s):  
Large Scale ◽  
Latent Trait ◽  
Theory Model ◽  
Estimation Accuracy ◽  
Cognitive Assessments ◽  
Collateral Information ◽  
Unfolding Model ◽  
Generalized Graded Unfolding Model ◽  
Scoring Accuracy ◽  
Trait Estimation

Collateral information has been used to address subpopulation heterogeneity and increase estimation accuracy in some large-scale cognitive assessments. The methodology that takes collateral information into account has not been developed and explored in published research with models designed specifically for noncognitive measurement. Because the accurate noncognitive measurement is becoming increasingly important, we sought to examine the benefits of using collateral information in latent trait estimation with an item response theory model that has proven valuable for noncognitive testing, namely, the generalized graded unfolding model (GGUM). Our presentation introduces an extension of the GGUM that incorporates collateral information, henceforth called Explanatory GGUM. We then present a simulation study that examined Explanatory GGUM latent trait estimation as a function of sample size, test length, number of background covariates, and correlation between the covariates and the latent trait. Results indicated the Explanatory GGUM approach provides scoring accuracy and precision superior to traditional expected a posteriori (EAP) and full Bayesian (FB) methods. Implications and recommendations are discussed.

GGUM2004: A Windows-Based Program to Estimate Parameters in the Generalized Graded Unfolding Model

2006 ◽  
Vol 30 (1) ◽  
pp. 64-65 ◽  
Author(s):  
James S. Roberts ◽  
Haw-ren Fang ◽  
Weiwei Cui ◽  
Yingji Wang
Keyword(s):  
Unfolding Model ◽  
Generalized Graded Unfolding Model

scholarly journals GGUM: An R Package for Fitting the Generalized Graded Unfolding Model

2018 ◽  
Vol 43 (2) ◽  
pp. 172-173 ◽  
Author(s):  
Jorge N. Tendeiro ◽  
Sebastian Castro-Alvarez
Keyword(s):  
Operating System ◽  
Item Response Theory ◽  
Item Response ◽  
Ideal Point ◽  
R Package ◽  
Model Fit ◽  
Data Matrix ◽  
Unfolding Model ◽  
Irt Model ◽  
Generalized Graded Unfolding Model

In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.

scholarly journals Constructing a Scale of Attitudes toward School Science Using the General Graded Unfolding Model

2013 ◽  
Vol 7 (4) ◽  
pp. 537
Author(s):  
Taghreed Hijazi ◽  
Zaid Bani Ata
Keyword(s):  
School Science ◽  
Attitude Scale ◽  
Response Format ◽  
Attitudes Toward School ◽  
Unfolding Model ◽  
Item Parameters ◽  
Generalized Graded Unfolding Model ◽  
Test Retest Reliability ◽  
Item Scale ◽  
Level Of Agreement

The present study aimed at constructing an attitude scale toward school science using the generalized graded unfolding model (GGUM). A 47-item scale (24 positive, 23 negative) with 4-point response format was used to measure attitudes toward science among 9th  (n=424) and 10th (n=420) grade students in 38 sections distributed randomly over 22 schools in Irbid district. Respondents selected one of four options to represent their level of agreement with each item. The findings support the hypothesis that the data form a single unidimensional unfolding model. Furthermore, the findings showed that the GGUM didn’t fit the data of 7 items, leaving the final scale with 40 items, where accurate estimates of these item parameters were derived and the GGUM was appropriate. Cronbach's alpha for the internal consistency, and the test retest reliability coefficients of the final scale were 0.932 and 0.875, respectively. 

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