Date of Award:

8-2022

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Mathematics and Statistics

Committee Chair(s)

Jürgen Symanzik

Committee

Jürgen Symanzik

Committee

Daniel Coster

Committee

D. Richard Cutler

Committee

John R. Stevens

Committee

Breanna Studenka

Abstract

Eye tracking is a process for measuring the movement of an individual’s eye(s) when that individual is looking at something. Many eye-tracking technologies exist to aid in calculating and recording data associated with what a person focuses their visual attention on. For example, eye-tracking technology can record points on an image that a person is looking at. Often the question arises as to whether two people, or groups of people, are looking at the same thing(s). This dissertation presents a new way (or test) to quantify those differences while taking into consideration the randomness associated with such data. Hence, the test can help to determine if the differences between what the two people, or groups of people, are looking at are caused by chance or not. However, the test is also useful to many other kinds of data similar to but outside of eye-tracking research. While this test takes longer for standard household computers to run than other alternative tests currently available, it is shown to be better in many cases at correctly identifying differences when those differences were not caused by randomness. The test is also better at identifying when the differences are caused by chance, and not necessarily by the people. The test is applied to eye-tracking data from a study held at Utah State University (USU), called the USU Posture Study, where many differences are found. The test is available online, and comes with a user manual and some examples of how to use it.

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