Name: Zhaoyu Wang

School of Psychology – Ph.D. Dissertation Defense Meeting

Date: Friday, April 17th, 2026

Time: 3:00 PM - 4:30 PM

Location: Virtual, click here

Dissertation Committee Chair/Advisor:

James Roberts, Ph.D. (Georgia Tech)

Dissertation Committee Members:

Susan Embretson, Ph.D. (Georgia Tech)

Brian Habing, Ph.D. (University of South Carolina)

Audrey Leroux, Ph.D. (Georgia Tech)

Dingjing Shi, Ph.D. (Georgia Tech)

Title: Integrating the Multidimensional Generalized Graded Unfolding Model (MGGUM) and Multidimensional Scaling (MDS) to Improve Estimation Accuracy and Precision 

Abstract

This dissertation develops a Hybrid Model to enhance the Multidimensional Generalized

Graded Unfolding Model (MGGUM) by integrating Multidimensional Scaling (MDS) to refine

item location parameter estimates and reduce sample size requirements. The MGGUM,

an extension of the unidimensional generalized graded unfolding model (GGUM), has been

applied across various psychological domains. It often requires large samples, especially with

multidimensional data, which leads to a difficulty in the practical application of the model in

social science research where smaller samples prevail. To address this limitation, the present

study incorporates additional information about MGGUM item locations via MDS scale values

derived from similarity judgments between stimulus pairs. This integration is expected to improve

the precision of item parameter estimates in the MGGUM. Specifically, the proposed model

framework simultaneously estimates the item parameters of MGGUM and item scale values from

MDS by connecting them through a multivariate normal distribution. The variance-covariance

matrix of the multivariate normal distribution specifies the relationship between the perceptual

item locations (MDS scale values derived from dissimilarities data) and preferential item locations

(MGGUM item locations derived from preference or [graded] disagree-agree response data).

This specification allows the information from these two sources to simultaneously contribute to

parameter estimation across models during an MCMC procedure.

We conducted a parameter recovery simulation study and a real data analysis to compare the

performance of the new (hybrid) model with that of the original MGGUM. Results demonstrated

that the Hybrid Model improved the precision of MGGUM item location estimates and indirectly

improved the estimation of other parameters. It also reduced data demand to some extent and

substantially reduced computation time. These improvements extend the practical application

universe and efficiency of the MGGUM framework.