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Understanding, Communicating, and Reducing Analytical Uncertainty : = Theory, Visualization Designs, and an Augmented Presentation System to Support Validation and Interpretation of a Multiverse Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Understanding, Communicating, and Reducing Analytical Uncertainty :/
其他題名:	Theory, Visualization Designs, and an Augmented Presentation System to Support Validation and Interpretation of a Multiverse Analysis.
作者:	Hall, Brian D.
面頁冊數:	1 online resource (128 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Theoretical physics. -
電子資源:	click for full text (PQDT)
ISBN:	9798382738215

Understanding, Communicating, and Reducing Analytical Uncertainty : = Theory, Visualization Designs, and an Augmented Presentation System to Support Validation and Interpretation of a Multiverse Analysis.
Hall, Brian D.

Understanding, Communicating, and Reducing Analytical Uncertainty :Theory, Visualization Designs, and an Augmented Presentation System to Support Validation and Interpretation of a Multiverse Analysis. - 1 online resource (128 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of Michigan, 2024.

Includes bibliographical references

Analyzing data is a complex, multi-step process, with multiple choices available at each step, such as whether and how to exclude outliers, what approach to use to operationalize a variable, or what model and parameters to apply. While it is often possible to exclude some choices as invalid, often many alternatives remain that are equally valid, and when these alternatives lead to divergent conclusions there exists what I term analytical uncertainty. Faced with this complexity and the practical demands for professional productivity, analysts often report either a single analysis or a small number of non-divergent supporting analyses, which can result in what has been called uncertainty laundering: misrepresenting uncertainty as if it were a known quantity. Yet accurately communicating analytical uncertainty, which is often non-quantified or non-quantifiable, in a way that is both practically useful and professionally acceptable often proves to be an exceedingly difficult task.In this dissertation, I explore ways to use data visualizations and interactive systems to support the assessment, communication, and reduction of analytical uncertainty. My aim is to pare away a slice of the ontological uncertainty present in empirical research and render it in such a way that not only can it be assessed and clearly communicated, but also so that it can be reduced.A review of literature on topics related to uncertainty led me to reconceptualize the method of multiverse analysis as a way to assess and communicate analytical uncertainty. A systematic review and critical analysis of published multiverse analysis reports resulted in the derivation of a taxonomy of multiverse analysis tasks, as well as the identification and detailed description of multiverse visualizations archetypes. The systematic review also resulted in the observation that a specific set of multiverse analysis tasks-validation tasks and interpretation tasks-are critical to making a multiverse analysis useful and meaningful, yet are also the tasks that are the least supported by existing archetypes and interactive systems. I conclude that these tasks may be particularly difficult and require multiple perspectives on the data, such that they are perhaps best supported by a set or series of visualizations. I then developed a prototype system is developed named AugMeet, which uses two techniques in combination to support validation and interpretation tasks: augmented presentation; and a specially designed series of interactive visualizations, including new designs, namely parameter-faceted outcome curves, outcome-faceted outcome curves, and twin-faceted residual plots. I evaluated this system by recording an approximately 25 minute presentation I gave using the AugMeet system, which was based on my own novel analysis of the hurricane gender-name multiverse created by previous authors [89, 81]; I then showed the recorded demonstration to seven experienced researchers, who I judged to be representative of the intended users for this type of system. Based on these interviews, I offer the following four conclusions: The augmented presentation aspects of AugMeet seemed to serve primarily as a way to make it easier for the audience to listen to and understand the information being conveyed to them about and through the visualizations themselves.Comparing residuals of twin universes elicits divergent thinking, even though the design intent of twin-faceted residual plots was to avoid spurious conclusions about the superiority of one multiverse option over another. Parameter-faceted outcome curves give perspective and focus first by providing an overview of all the choices that have been identified as worth considering, and then by providing visual features to identify what is important (potentially impactful) and what is not.AugMeet supports the iterative group effort necessary to complete difficult validation and interpretation tasks, and participants found particular value in the demonstrated workflow that featured progressive iteration around a multiverse in a group settingAnalytical uncertainty is far from trivial to assess, communicate, and reduce, but the techniques developed and explicated within this dissertation show how it is indeed possible and can be practicable.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382738215Subjects--Topical Terms:

1180318
Theoretical physics.
Subjects--Index Terms:

Data visualizationIndex Terms--Genre/Form:

554714
Electronic books.

Understanding, Communicating, and Reducing Analytical Uncertainty : = Theory, Visualization Designs, and an Augmented Presentation System to Support Validation and Interpretation of a Multiverse Analysis.
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