Open AccessA Model of Cross-Disciplinary Communication for Collaborative Statisticians: Implications for Curriculum Design
Author(s) -
Gregory P. Samsa,
Thomas W. LeBlanc,
Susan C. Locke,
Jesse D. Troy,
GinaMaria Pomann
Publication year - 2018
Publication title -
journal of curriculum and teaching
Language(s) - English
Resource type - Journals
eISSN - 1927-2685
pISSN - 1927-2677
DOI - 10.5430/jct.v7n2p1
Subject(s) - computer science , curriculum , task (project management) , discipline , set (abstract data type) , bridge (graph theory) , mathematics education , management science , data science , psychology , pedagogy , sociology , medicine , social science , management , economics , programming language
The ability to bridge multiple disciplines is critical to the successful practice of collaborative statistics, yet theliterature on statistical education devotes relatively little attention to how this skill can be taught. Our goal here is todescribe a general conceptual framework within which a curriculum on communication and leadership couldultimately be organized.The primary research question pertains to whether an actionable model of cross-disciplinary communication forcollaborative statisticians can be developed, and our task here is to describe such a model and also to illustrate its use.Within this model most communications either share or request information. For example, statisticians might provideinformation about statistics (e.g., specific statistical approaches, general statistical principles), comment on theclinician’s understanding of statistics, share their understanding of clinical content, and request information (e.g.,about clinical content, the design and execution of the study being discussed, etc.). Clinical investigators contributean analogous set of components. In addition, a critical element to the interaction is the higher-level task ofdeveloping a mutually understood agreement about the work to be performed: in essence, proposing and negotiatingsuch an agreement.The model is illustrated using a case study, and general qualitative feedback from investigators who performed thecase study was obtained, commenting on both successful and unsuccessful interactions with statisticians.Implications for curriculum development are discussed.