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Visualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for ‘concept-driven’ visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study.
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Hypothesis Formalization: Empirical Findings, Software Limitations, and Design Implications
Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as hypothesis formalization . In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.
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- Award ID(s):
- 1901386
- PAR ID:
- 10355052
- Date Published:
- Journal Name:
- ACM Transactions on Computer-Human Interaction
- Volume:
- 29
- Issue:
- 1
- ISSN:
- 1073-0516
- Page Range / eLocation ID:
- 1 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3476980
