TY - JOUR
T1 - bmggum
T2 - An R Package for Bayesian Estimation of the Multidimensional Generalized Graded Unfolding Model With Covariates
AU - Tu, Naidan
AU - Zhang, Bo
AU - Angrave, Lawrence
AU - Sun, Tianjun
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2021/10
Y1 - 2021/10
N2 - 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.
AB - 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.
KW - Bayesian estimation
KW - Hamiltonian Monte Carlo
KW - ideal point model
KW - item response theory
KW - multidimensional generalized graded unfolding model
UR - http://www.scopus.com/inward/record.url?scp=85115088209&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115088209&partnerID=8YFLogxK
U2 - 10.1177/01466216211040488
DO - 10.1177/01466216211040488
M3 - Article
C2 - 34866713
AN - SCOPUS:85115088209
SN - 0146-6216
VL - 45
SP - 553
EP - 555
JO - Applied Psychological Measurement
JF - Applied Psychological Measurement
IS - 7-8
ER -