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

Naidan Tu, Bo Zhang, Lawrence Angrave, Tianjun Sun

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)553-555
Number of pages3
JournalApplied Psychological Measurement
Volume45
Issue number7-8
DOIs
StatePublished - Oct 2021

Keywords

  • Bayesian estimation
  • Hamiltonian Monte Carlo
  • ideal point model
  • item response theory
  • multidimensional generalized graded unfolding model

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

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