The Q1Q2Q3 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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Journal of Statistics and Data Science Education |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26939169.2025.2475775 |
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| _version_ | 1849762883507847168 |
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| author | Eric A. Vance Ilana M. Trumble Jessica L. Alzen Leanna L. House |
| author_facet | Eric A. Vance Ilana M. Trumble Jessica L. Alzen Leanna L. House |
| author_sort | Eric A. Vance |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8f10c19e4cc24e51b99873f932c81cd8 |
| institution | DOAJ |
| issn | 2693-9169 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Statistics and Data Science Education |
| spelling | doaj-art-8f10c19e4cc24e51b99873f932c81cd82025-08-20T03:05:35ZengTaylor & Francis GroupJournal of Statistics and Data Science Education2693-91692025-04-0112110.1080/26939169.2025.2475775The Q1Q2Q3 Workflow for Statistics and Data Science CollaborationsEric A. Vance0Ilana M. Trumble1Jessica L. Alzen2Leanna L. House3Laboratory for Interdisciplinary Statistical Analysis, Department of Applied Mathematics, University of Colorado Boulder, Boulder, COLaboratory for Interdisciplinary Statistical Analysis, Department of Applied Mathematics, University of Colorado Boulder, Boulder, COCenter for Assessment, Design, Research and Evaluation, University of Colorado Boulder, Boulder, CODepartment of Statistics, Virginia Tech, Blacksburg, VAWe 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.https://www.tandfonline.com/doi/10.1080/26939169.2025.2475775Data science educationData science lifecycleEthicsStatistical collaborationStatistical consultingStatistical practice |
| spellingShingle | Eric A. Vance Ilana M. Trumble Jessica L. Alzen Leanna L. House The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations Journal of Statistics and Data Science Education Data science education Data science lifecycle Ethics Statistical collaboration Statistical consulting Statistical practice |
| title | The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations |
| title_full | The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations |
| title_fullStr | The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations |
| title_full_unstemmed | The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations |
| title_short | The Q1Q2Q3 Workflow for Statistics and Data Science Collaborations |
| title_sort | q1q2q3 workflow for statistics and data science collaborations |
| topic | Data science education Data science lifecycle Ethics Statistical collaboration Statistical consulting Statistical practice |
| url | https://www.tandfonline.com/doi/10.1080/26939169.2025.2475775 |
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