Data Science | MS Thesis Defense | Russ Davis

Monday, April 24, 2023
4:00 pm to 5:00 pm

United States


MS Thesis Defense 

Russ Davis

Monday, April 24th | 4:00PM - 5:30PM

Zoom Link:


Advisor: Lane Harrison, Associate Professor, Computer Science / Data Science
Reader: Randy Paffenroth, Associate Professor, Mathematical Sciences / Data Science

Title: Assessing Individual Differences in Graphical Perception

Abstract: Data is often presented in the form of graphical visualizations rather than as raw data, with encodings frequently chosen to optimize for accuracy of interpretation by the audience. Visualization guidelines have been drafted to help designers and creators select visualizations that optimize the reader’s ability to understand it. However, most visualization guidelines are derived from studies that focus on population-level rankings of accuracy, disregarding possible individual differences in people's ability to interpret visualizations. This thesis considers variations in individual performance by replicating and extending Cleveland & McGill’s widely-studied visualization experiment. By implementing Bayesian multilevel regression, we generate models that facilitate exploration of differences between individual participants and between each visualization type. We confirm that a substantial percentage of individuals show accuracy judgments that deviate from the canonical rankings. We discuss between-individual differences as a relevant factor for design effectiveness, with respect to its capacity to highlight individual variation from population-level aggregates, and with its ability to differentiate factors to between-individual variation; implications for research focused on providing guidance to visualization designers; and proposed further modifications to research in the mode of Cleveland & McGill.



Data Science
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