Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

Jun, Eunice; Seo, Audrey; Heer, Jeffrey; Just, René

doi:10.1145/3491102.3501888

Cited by 12 publications

(2 citation statements)

References 26 publications

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“…Investigating how to integrate model recommendations into a tool like EVM is a natural follow-up to our work. Recommendations should account for what is known about the user's understanding of the DGP at the time of recommendation, similar to the way that tools like Tisane [26] and Visual Causality Analyst [57,58] anchor model suggestions on knowledge elicited from users. If the user's preferred model at a given moment during analysis represents their traversal of a "model space" they are searching, recommendations should be proximal to and informed by the user's current model.…”

Section: Ongoing and Future Workmentioning

confidence: 99%

MetaExplorer : Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

Kale

Lee

Goan

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Fig. 1: Model checks modify the typical visual analytics workflow by enabling users to assess the plausibility of interpretations of discovered patterns. A ⃝ The analyst discovers that accounting for time spent studying appears to help explain student absences. B ⃝ The analyst facets this view by the highest level of education achieved by each student's guardian. He wonders if study time is predictive of absences after accounting for guardian education. C⃝ The analyst specifies models asserting that absences are explained by either: guardian education alone (orange); or guardian education and study time (red). Seeing that predictions from the second model do a better job of capturing the largest numbers of absences, he concludes that both guardian education and study time are important explanatory variables.

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Section: Ongoing and Future Workmentioning

confidence: 99%

MetaExplorer : Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

Kale

Lee

Goan

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…However, supporting such procedures requires software representation of epistemic uncertainty around analysis choices [33,38]. Recent work in human-computer interaction addresses this challenge primarily by attempting to guide analysts in selecting among possible models [32,39,69,72] and surfacing provenance about measurements [44,60].…”

Section: Reasoning With Epistemic Uncertainty In Data Analysismentioning

confidence: 99%

MetaExplorer: Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

Kale,

Lee,

Goan

et al. 2023

Preprint

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Figure 1: MetaExplorer provides a guided process for literature review and meta-analysis with an emphasis on documenting sources of epistemic uncertainty and choosing how to address them during statistical inference. The workflow proceeds in stages from Scoping and literature review where MetaExplorer elicits information about each study, to Triage and study grouping where the user resolves sources of epistemic uncertainty, and finally to Meta-analysis where the user views results alongside contextualizing uncertainty.

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Merits and Limits of Preregistration for Visualization Research

Besançon,

Nosek,

Haven

et al. 2024

2024 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)

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The replication crisis has spawned a revolution in scientific methods, aimed at increasing the transparency, robustness, and reliability of scientific outcomes. In particular, the practice of preregistering study designs has shown important advantages. Preregistration can help limit questionable research practices, as well as increase the success rate of study replications. Many fields have now adopted preregistration as a default expectation for published studies. In 2022, we set up a panel "Merits and Limits of User Study Preregistration" with the overall goal of explaining the concept of preregistration to a wide VIS audience and discussing its suitability for visualization research. We report on the arguments and discussion of this panel in the hope that it can benefit the visualization community at large. All materials and a copy of this paper are available on our OSF repository at https://osf.io/wes57/.

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Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

Cited by 12 publications

References 26 publications

MetaExplorer : Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

MetaExplorer : Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

MetaExplorer: Facilitating Reasoning with Epistemic Uncertainty in Meta-analysis

Merits and Limits of Preregistration for Visualization Research

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