Evaluating the Alignment of a Data Analysis between Analyst and Audience Article Swipe
Lucy D’Agostino McGowan
,
Roger D. Peng
,
Stephanie C. Hicks
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.07616
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.07616
A challenge that data analysts face is building a data analysis that is useful for a given consumer. Previously, we defined a set of principles for describing data analyses that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept that we call the alignment of a data analysis between the data analyst and a consumer. We define a successfully aligned data analysis as the matching of principles between the analyst and the consumer for whom the analysis is developed. In this paper, we propose a statistical model for evaluating the alignment of a data analysis and describe some of its properties. We argue that this framework provides a language for characterizing alignment and can be used as a guide for practicing data scientists and students in data science courses for how to build better data analyses.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.07616
- https://arxiv.org/pdf/2312.07616
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389767752
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389767752Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.07616Digital Object Identifier
- Title
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Evaluating the Alignment of a Data Analysis between Analyst and AudienceWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-11Full publication date if available
- Authors
-
Lucy D’Agostino McGowan, Roger D. Peng, Stephanie C. HicksList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.07616Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.07616Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.07616Direct OA link when available
- Concepts
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Computer science, Data science, Data set, Set (abstract data type), Matching (statistics), Data analysis, Variation (astronomy), Data mining, Information retrieval, Artificial intelligence, Physics, Astrophysics, Statistics, Mathematics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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