skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Goals for Statistics and Data Science Collaborations
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.  more » « less
Award ID(s):
1955109 2022138
PAR ID:
10227760
Author(s) / Creator(s):
Date Published:
Journal Name:
JSM Proceedings, Statistical Consulting Section
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, Journal of Statistics and Data Science Education)
    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. 
    more » « less
  2. ; (, IASE)
    Bulmer, Michael; Finch, Sue (Ed.)
    By learning collaboration skills, statisticians and data scientists can more effectively collaborate with others who possess the skills they may lack. We describe the ASCCR framework, which was developed in the United States for teaching and learning collaboration skills. We hypothesize which aspects of this framework are universal and which are culturally dependent. We provide an example of our initial attempt to translate the Structure component of ASCCR into the Indonesian language and culture. The new acronym of SABAR can help Indonesian students learn, remember, and use specific skills for structuring, organizing, and conducting collaboration meetings. How to effectively teach collaboration skills to Indonesian students in statistics and data science will require more work and is the subject of future research. 
    more » « less
  3. ; (, JSM Proceedings, Statistical Consulting Section)
    The questions we ask and the way in which we ask them can make all the difference in how successful we are in meetings, in collaborations, and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? In this paper, we develop a theory for asking great questions that elicit information useful for accomplishing the tasks of a collaborative project and also strengthen the statistician-domain expert relationship. We deconstruct asking great questions into three parts: the question, the answer, and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question, follow the question with behaviors and actions consistent with the prefaced words including actions such as listening, paraphrasing, and summarizing; and model a collaborative relationship via the asking of a great question. We provide practical guidelines for learning these skills so that statisticians can improve their statistical collaboration skills and thus increase their impact to help address societal challenges. 
    more » « less
  4. ; ; (, JSM Proceedings, Statistical Consulting Section)
    As practitioners and teachers of statistical consulting and collaboration, how do we assess the effectiveness of ours and our students’ engagement on projects with domain experts? We propose that assessments of the effectiveness of statistical collaborations should be based on the four areas of attitude, skills, performance, and improvement. In this brief paper, we describe several ways for conducting assessments in these four areas and conclude with a call for the statistics and data science education community to build upon these ideas. 
    more » « less
  5. ; ; ; (, Stat)
    The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email