bmggum: An R Package for Bayesian Estimation of the Multidimensional Generalized Graded Unfolding Model With Covariates

2021 ◽  
pp. 014662162110404
Author(s):  
Naidan Tu ◽  
Bo Zhang ◽  
Lawrence Angrave ◽  
Tianjun Sun
Keyword(s):  
Ideal Point ◽  
R Package ◽  
Point Model ◽  
Unfolding Model ◽  
The Past ◽  
Item Parameters ◽  
Generalized Graded Unfolding Model ◽  
Constrained Models ◽  
Fully Bayesian ◽  
Noncognitive Constructs

Over the past couple of decades, there has been an increasing interest in adopting ideal point models to represent noncognitive constructs, as they have been demonstrated to better measure typical behaviors than traditional dominance models do. The generalized graded unfolding model ( GGUM) has consistently been the most popular ideal point model among researchers and practitioners. However, the GGUM2004 software and the later developed GGUM package in R can only handle unidimensional models despite the fact that many noncognitive constructs are multidimensional in nature. In addition, GGUM2004 and the GGUM package often yield unreasonable estimates of item parameters and standard errors. To address these issues, we developed the new open-source bmggum R package that is capable of estimating both unidimensional and multidimensional GGUM using a fully Bayesian approach, with supporting capabilities of stabilizing parameterization, incorporating person covariates, estimating constrained models, providing fit diagnostics, producing convergence metrics, and effectively handling missing data.

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. 

A Comparison of the LR and DFIT Frameworks of Differential Functioning Applied to the Generalized Graded Unfolding Model

2011 ◽  
Vol 35 (8) ◽  
pp. 623-642 ◽  
Author(s):  
Nathan T. Carter ◽  
Michael J. Zickar
Keyword(s):  
Ideal Point ◽  
Poor Performance ◽  
Theory Model ◽  
Future Research ◽  
Differential Functioning ◽  
Unfolding Model ◽  
Point Conception ◽  
Item Functioning ◽  
Generalized Graded Unfolding Model ◽  
Item Responses

Recently, applied psychological measurement researchers have become interested in the application of the generalized graded unfolding model (GGUM), a parametric item response theory model that posits an ideal point conception of the relationship between latent attributes and observed item responses. Little attention has been given to considerations for the detection of differential item functioning (DIF) under the GGUM. In this article, the authors present a Monte Carlo simulation meant to assess the efficacy of the likelihood ratio (LR) and differential functioning of items and tests (DFIT) frameworks, two popular ways of detecting DIF. Findings indicate a marked superiority of the LR approach over DFIT in terms of true and false positive rates under the GGUM. The discussion centers on possible explanations for the poor performance of the DFIT framework in detecting DIF under the GGUM and addresses limitations of the current study as well as future research directions.

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.

scholarly journals BIC Extensions for Order-constrained Model Selection

2019 ◽  
pp. 004912411988245 ◽  
Author(s):  
J. Mulder ◽  
A. E. Raftery
Keyword(s):  
Model Selection ◽  
Model Comparison ◽  
Social Science Research ◽  
Information Criterion ◽  
Science Research ◽  
R Package ◽  
Local Unit ◽  
European Values ◽  
Constrained Model ◽  
Constrained Models

The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC, however, is not suitable for evaluating models with order constraints on the parameters of interest. This article explores two extensions of the BIC for evaluating order-constrained models, one where a truncated unit information prior is used under the order-constrained model and the other where a truncated local unit information prior is used. The first prior is centered on the maximum likelihood estimate, and the latter prior is centered on a null value. Several analyses show that the order-constrained BIC based on the local unit information prior works better as an Occam’s razor for evaluating order-constrained models and results in lower error probabilities. The methodology based on the local unit information prior is implemented in the R package “BICpack” which allows researchers to easily apply the method for order-constrained model selection. The usefulness of the methodology is illustrated using data from the European Values Study.

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