IEEE VIS 2024 Content: Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in the Light of Advanced AI

Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in the Light of Advanced AI

Daniel Weiskopf - University of Stuttgart, Stuttgart, Germany

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Room: Bayshore I

2024-10-14T16:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T16:00:00Z
Exemplar figure, described by caption below
Illustration of the approach that helps bridge quantitative and qualitative methods for visualization research. The schematic process comprises the research question, study design and execution, and iterative analysis of (possibly multimodal) study data. The key part is the analysis loop that keeps on transforming and enriching data with additional semantics to derive new data representations. Through the process, information is obtained at higher and higher levels of understanding. The analysis loop may consist of AI-based processing, user intervention, or a combination thereof.
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Abstract

This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how we can bridge between the different methods in an integrated process of analyzing user study data. To this end, a process model of - potentially iterated - semantic enrichment of data is proposed. This joint perspective of data and semantics facilitates the integration of quantitative and qualitative methods. The model is motivated by examples of prior work, especially in the area of eye tracking user studies and coding data-rich observations. Finally, there is a discussion of open issues and research opportunities in the interplay between AI and qualitative and quantitative methods for visualization research.