DOI

10.17077/etd.8ddrejir

Document Type

Dissertation

Date of Degree

Spring 2018

Access Restrictions

Access restricted until 07/03/2019

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biostatistics

First Advisor

Jacob J. Oleson

Second Advisor

Bob McMurray

First Committee Member

Brian J. Smith

Second Committee Member

Grant Brown

Third Committee Member

Daniel Sewell

Abstract

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.

Keywords

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

Pages

viii, 135 pages

Bibliography

Includes bibliographical references (pages 130-135).

Copyright

Copyright © 2018 Michael Thomas Seedorff

Available for download on Wednesday, July 03, 2019

Included in

Biostatistics Commons

Share

COinS