Document Type


Date of Degree

Spring 2018

Access Restrictions

Access restricted until 07/03/2019

Degree Name

PhD (Doctor of Philosophy)

Degree In


First Advisor

Oleson, Jacob J.

Second Advisor

McMurray, Bob

First Committee Member

Smith, Brian J.

Second Committee Member

Brown, Grant

Third Committee Member

Sewell, Daniel


Data resulting from eye-tracking experiments allows researchers to analyze the decision making process as study participants consider alternative items prior to the ultimate end point selection. The aim of such an analysis is to extract the underlying cognitive decision making process that develops throughout the experiment. Resulting data can be difficult to analyze, however, as eye-tracking curves have very high autocorrelation values which consists of measurements that are milliseconds apart, as mandated by the nature of eye movements. We propose an analytic approach to eye-tracking data that tests for statistically significant differences at every time point along the curve while calculating an appropriate familywise error rate correction which is based upon an autoregressive correlation assumption of the test statistics. Our technique has been implemented in the R package BDOTS with various extensions relevant to the real-world analysis of highly correlated nonlinear data. A popular alternative approach to analyzing eye-tracking data is to fit mixed models to the area under the curve. Through simulation studies we provide evidence for the benefit of using information criterion measures in selection of the random effects structure and make an argument against current industry-standard approaches such as sequential likelihood ratio tests or always using a maximal random effects structure.


Eye-tracking, Familywise error rate, Nonlinear, Psycholinguistics, Time series


viii, 135 pages


Includes bibliographical references (pages 130-135).


Copyright © 2018 Michael Thomas Seedorff

Included in

Biostatistics Commons