Master’s Thesis Proposal Meeting
Date: Wednesday, May 1, 2019
Time: 12:00pm
Location: J.S. Coon Building, room 148
 
Advisor:
Susan Embretson, Ph.D. (Georgia Tech)
 
Thesis Committee Members:
Susan Embretson, Ph.D. (Georgia Tech)
Rick Thomas, Ph.D. (Georgia Tech)
Michael Hunter, Ph.D. (Georgia Tech)
 
Title: A Study of a Fit Index for Explanatory Item Response Theory Models

Abstract: Applying explanatory item response theory (IRT) models, such as the linear logistic test model (LLTM; Fischer, 1973) is advantageous when designing and selecting items. Likelihood ratio chi square tests for nested models are typically used to determine model significance.  Multiple correlations of item difficulties estimated with the explanatory predictors are often used to provide further information about model quality.  However, this approach is not statistically justifiable, since the effective sample size becomes the number of items. A simulation study was conducted to compare an explanatory item response theory fit statistic, Δ (Embretson, 1997; 2016), to traditionally used fit indices (nested model likelihoods and limited information multiple correlations) for assessing model quality.  Simulation conditions include varying test length, item difficulty and the number of predictors.