Empirical analysis of the subjective impressions and objective measures of domain scientists' visual analytic judgments

Aritra Dasgupta, Susannah Burrows, Kyungsik Han, Philip J. Rasch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists' subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focus on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of: i) relationships among scientists' familiarity, their perceived levels of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.

Original languageEnglish (US)
Title of host publicationCHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Pages1193-1204
Number of pages12
ISBN (Electronic)9781450346559
DOIs
StatePublished - May 2 2017
Externally publishedYes
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings
Volume2017-May

Conference

Conference2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Country/TerritoryUnited States
CityDenver
Period5/6/175/11/17

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Keywords

  • Climate
  • Familiarity
  • Information visualization
  • Preference
  • Slope plot
  • Taylor plot
  • Trust
  • Visual comparison

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