Naidan Tu, Bo Zhang, Lawrence Angrave, Tianjun Sun
{"title":"<i>bmggum</i>: An R Package for Bayesian Estimation of the Multidimensional Generalized Graded Unfolding Model With Covariates.","authors":"Naidan Tu, Bo Zhang, Lawrence Angrave, Tianjun Sun","doi":"10.1177/01466216211040488","DOIUrl":null,"url":null,"abstract":"<p><p>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 (<i>GGUM</i>) has consistently been the most popular ideal point model among researchers and practitioners. However, the GGUM2004 software and the later developed <i>GGUM</i> package in R can only handle unidimensional models despite the fact that many noncognitive constructs are multidimensional in nature. In addition, GGUM2004 and the <i>GGUM</i> package often yield unreasonable estimates of item parameters and standard errors. To address these issues, we developed the new open-source <i>bmggum</i> R package that is capable of estimating both unidimensional and multidimensional <i>GGUM</i> 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.</p>","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":"45 7-8","pages":"553-555"},"PeriodicalIF":1.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640348/pdf/10.1177_01466216211040488.pdf","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/01466216211040488","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/9/15 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PSYCHOLOGY, MATHEMATICAL","Score":null,"Total":0}
引用次数: 3
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.
期刊介绍:
Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.