2026-09-19T21:54:36Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1627022025-09-19T04:49:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Fox, Amy Rae
Satyanarayan, Arvind
Hua, Dana
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-09-18T14:28:06Z
2025-09-18T14:28:06Z
2025-05
2025-06-23T14:02:15.665Z
https://hdl.handle.net/1721.1/162702
Research in public data communication typically focuses on improving the processes of encoding and decoding, answering the question of how to design a visualization to best communicate information to an audience. However, by treating visual communications as simply conduits for information, we ignore an important aspect of how people interact with communications. We ignore the attitudes – the thoughts, feelings, and intentions toward action – a person may form from communicative artifacts based on their personal values and experiences. Recent research has demonstrated that—much like natural language—readers of visualizations make social attributions: inferences about the identities and characteristics of an artifact’s makers, modes of distribution, and tools of production. In this thesis, I contribute a method to systematically map the visualization attitudes of an individual and the associated ideologies of their sociocultural group, by adapting the repertory grid technique from clinical psychology, to the context of data visualization. I demonstrate the effectiveness of this mixed methods approach by eliciting both the attitudes towards a visualization most salient to an individual, and the design features of the visualization that inform each attitude. This method offers a new way of exploring the content and latent structure of visualization attitudes, which opens new avenues for socioculturally-informed and intervention-driven research in data visualization.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Eliciting Visualization Attitudes with Repertory Grids
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Research in public data communication typically focuses on improving the processes of encoding and decoding, answering the question of how to design a visualization to best communicate information to an audience. However, by treating visual communications as simply conduits for information, we ignore an important aspect of how people interact with communications. We ignore the attitudes – the thoughts, feelings, and intentions toward action – a person may form from communicative artifacts based on their personal values and experiences. Recent research has demonstrated that—much like natural language—readers of visualizations make social attributions: inferences about the identities and characteristics of an artifact’s makers, modes of distribution, and tools of production. In this thesis, I contribute a method to systematically map the visualization attitudes of an individual and the associated ideologies of their sociocultural group, by adapting the repertory grid technique from clinical psychology, to the context of data visualization. I demonstrate the effectiveness of this mixed methods approach by eliciting both the attitudes towards a visualization most salient to an individual, and the design features of the visualization that inform each attitude. This method offers a new way of exploring the content and latent structure of visualization attitudes, which opens new avenues for socioculturally-informed and intervention-driven research in data visualization.</Abstract>
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