Education and Human Sciences, College of (CEHS)
First Advisor
Rafael De Ayala
Date of this Version
5-2017
Citation
Orley, G. (2016). Multiple Imputation of the Guessing Parameter in the Case of Missing data. Unpublished master’s thesis, University of Nebraska - Lincoln, Lincoln, Nebraska.
Abstract
Missing data are a significant problem in testing. Research into strategies for dealing with it have yielded no clear consensus about the best approach to take. Accuracy of ability estimates, fairness and scoring transparency are affected by the choice of missing data handling technique. In this simulation study, we propose a technique of multiple imputation of the guessing parameter using both item difficulty and individual ability estimates. This approach was compared to several other popular strategies for imputing values, such as: treating the item as incorrect, imputing a guessing parameter of 0.5, proportion correct imputation, multiple imputation of responses, and multiple imputation of the guessing parameter value. Assessments of the accuracy of ability estimates for each technique were examined in terms of root mean-squared error (RMSE) and bias. These dependent variables were calculated both across the ability continuum and as a function of theta. The implications of these results for real-world testing are discussed.
Advisor: Rafael De Ayala
Comments
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Arts, Major: Educational Psychology, Under the Supervision of Professor Rafael De Ayala. Lincoln, Nebraska: May, 2017
Copyright (c) 2017 Grant J. Orley