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Statisticians have a long history of consulting and collaborating with experts from a variety of fields. Now with the rise of data science, collaborating across disciplines is both more important and more prevalent than ever before. This paper examines the goals of statistics and data science collaborations and uses the ASCCR (Attitude-Structure-Communication-Content-Relationship) Framework for Collaboration to connect these goals. Specifically, we propose that a useful way of guiding consultations and collaborations is for statisticians and data scientists to work toward two terminal goals of a collaboration: to make a deep contribution to the field and create a strong relationship with the domain expert. To help in achieving these goals, statisticians and data scientists should strive to achieve three instrumental goals: adopt an attitude of collaboration, provide effective structure for the collaboration, and communicate to create shared understanding. We show how these five goals map onto the ASCCR Frame, how they are connected to each other, and how to have conversations about these goals. The goal of this paper is to show statisticians and data scientists how they can become more effective collaborators by providing motivation for using the ASCCR framework to improve their practice of statistics and data science.
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The Q 1 Q 2 Q 3 Workflow for Statistics and Data Science Collaborations
We develop, advance, and promote a previously existing framework called the Qualitative-Quantitative-Qualitative workflow (Q1Q2Q3, pronounced “Q-Q-Q”) to systematically guide the content of interdisciplinary collaborations and improve the teaching of statistics and data science. The Q1Q2Q3 workflow is designed to help statisticians and data scientists develop skills and techniques for collaboration to work with domain experts across academic fields, industry sectors, and organizations. The Q1Q2Q3 workflow explicitly emphasizes the importance of the qualitative context of a project, as well as the qualitative interpretation of quantitative findings. We explain Q1Q2Q3 and provide guidance for implementing each stage of the workflow. We describe how we teach Q1Q2Q3 within a statistics and data science collaboration course and present data evaluating its effectiveness. We also describe how Q1Q2Q3 can be useful for educators teaching introductory, projects-based, and technical statistics and data science courses. We believe that the Q1Q2Q3 workflow is an easy-to-implement technique that is beneficial and necessary for statistics and data science education and practice. It can be used to weave ethics into each stage of practice so that statisticians and data scientists can successfully transform evidence into action for the benefit of society.
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- PAR ID:
- 10630709
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- Journal of Statistics and Data Science Education
- ISSN:
- 2693-9169
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1080/26939169.2025.2475775
