Marginal Bayesian parameter estimation in the multidimensional generalized graded unfolding model

Thompson, Vanessa Marie

Title:

Marginal Bayesian parameter estimation in the multidimensional generalized graded unfolding model

dc.contributor.advisor	Roberts, James S.
dc.contributor.author	Thompson, Vanessa Marie
dc.contributor.committeeMember	Embretson, Susan E.
dc.contributor.committeeMember	James, Lawrence R.
dc.contributor.committeeMember	Spieler, Daniel H.
dc.contributor.committeeMember	Parsons, Charles K.
dc.contributor.department	Psychology
dc.date.accessioned	2015-06-08T18:10:20Z
dc.date.available	2015-06-09T05:30:07Z
dc.date.created	2014-05
dc.date.issued	2014-02-04
dc.date.submitted	May 2014
dc.date.updated	2015-06-08T18:10:20Z
dc.description.abstract	The Multidimensional Generalized Graded Unfolding Model (MGGUM) is a proximity-based, noncompensatory item response theory (IRT) model with applications in the context of attitude, personality, and preference measurement. Model development used fully Bayesian Markov Chain Monte Carlo (MCMC) parameter estimation (Roberts, Jun, Thompson, & Shim, 2009a; Roberts & Shim, 2010). Challenges can arise while estimating MGGUM parameters using MCMC where the meaning of dimensions may switch during the estimation process and difficulties in obtaining informative starting values may lead to increased identification of local maxima. Furthermore, researchers must contend with lengthy computer processing time. It has been shown alternative estimation methods perform just as well as, if not better than, MCMC in the unidimensional Generalized Graded Unfolding Model (GGUM; Roberts & Thompson, 2011) with marginal maximum a posteriori (MMAP) item parameter estimation paired with expected a posteriori (EAP) person parameter estimation being a viable alternative. This work implements MMAP/EAP parameter estimation in the multidimensional model. Additionally, item location initial values are derived from detrended correspondence analysis (DCA) based on previous implementation of correspondence analysis in the GGUM (Polak, 2011). A parameter recovery demonstrates the accuracy of two-dimensional MGGUM MMAP/EAP parameter estimates and a comparative analysis of MMAP/EAP and MCMC demonstrates equal accuracy, yet much improved efficiency of the former method. Analysis of real attitude measurement data also provides an illustrative application of the model.
dc.description.degree	Ph.D.
dc.embargo.terms	2015-05-01
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/53411
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Estimation
dc.subject	Item response theory
dc.title	Marginal Bayesian parameter estimation in the multidimensional generalized graded unfolding model
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Roberts, James S.
local.contributor.corporatename	College of Sciences
local.contributor.corporatename	School of Psychology
relation.isAdvisorOfPublication	71a8755b-45fb-4904-8d8d-c6d3f95b14a9
relation.isOrgUnitOfPublication	85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication	768a3cd1-8d73-4d47-b418-0fc859ce897d
thesis.degree.level	Doctoral