Name: Yan Yan

Master’s Thesis Defense Meeting
Date: Wednesday, September 9, 2020
Time: 12:00pm
Location: Virtual, https://bluejeans.com/1338493093
 
Advisor:
Susan E. Embretson, Ph.D. (Georgia Tech)
 
Thesis Committee Members:
Susan E. Embretson, Ph.D. (Georgia Tech)
James S. Roberts, Ph.D. (Georgia Tech)
Michael D. Hunter, Ph.D. (Georgia Tech)
 
Title: Vertical Equating with Longitudinal Data: Unidimensional and Multidimensional Item Response Theory Models 

Abstract: Vertical equating is one context in which item response theory (IRT) model fit is important to evaluate and the measurement of change in students’ performance between test occasions is a central topic in educational research and assessment. The aim of the study presented here is to examine the performances of several multidimensional (MRMLC and SLTM) and unidimensional (Rasch and 2PL) IRT models on linking item parameters in a longitudinal design, in which there are no common items across the occasions. Both an empirical study with real data and a simulation study were conducted. It was found that the multidimensional IRT models for learning and change yielded more plausible results for equating and they have certain advantages over the standard unidimensional IRT models.