Date of Award

May 2021

Document Type


Degree Name

Doctor of Philosophy (PhD)


Human Factors Psychology

Committee Member

Christopher C Pagano

Committee Member

Eric R Muth

Committee Member

Patrick J Rosopa

Committee Member

Steven V Miller


The purpose of this dissertation was to analyze and disseminate a combined dataset created from one laboratory’s work over years of experiments on simulator sickness (SS) in head-mounted displays (HMDs). As the growth of HMDs continues, there is a need to re-examine conclusions from earlier SS research. For several years, our lab used the same research paradigm and questionnaires to study SS resulting from HMD latency characteristics. This approach offers stronger statistical evidence for prior conclusions and the opportunity to examine higher order SS symptomatology. The dataset contains 623 unique participants from 10 studies, yielding 875 exposures to our stimuli. The current analysis of this dataset had four objectives: 1) to examine the contributions of sex and system latency to SS; 2) to identify time-series profiles of SS responses; 3) to examine the prevalence of symptom clusters in short HMD exposures; and 4) to evaluate the similarity between measures of simulator and motion sickness. While we found that varying latencies are more sickening when examining our combined work, the effect of sex appears null. Our dataset demonstrated variability in participants over time and in symptoms, but evidence converged to show “a rising tide lifts all boats” and sickness severity is what defines the profiles. Last, we contribute a comparison of the SSQ and MSAQ and their relative strengths and weakness, while both measure the overall construct of SS, their differences should be used to determine the necessary and sufficient conditions for the next SS metric. Overall, this approach to a large body of SS observations provides methodology to be applied to other programs of research with SS. Using common methodology over years of work allowed for new analyses and findings on SS, reflecting variability in participants often missing from related work. Upon completion of this work, we will publish the dataset for use in future SS research and meta-analyses.



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