Training with the Mastery Rubric for Statistical Literacy to promote rigor and reproducibility across scientific disciplines: Making the journal club educational

Rochelle E. Tractenberg
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Rochelle E. Tractenberg: Georgetown University

No 8jn3e_v2, SocArXiv from Center for Open Science

Abstract: Statistical literacy is critically underappreciated across scientific disciplines, and is not part of the training in graduate programs even when “statistics” or “statistical methods” are. It is common to teach “introductory statistics” courses to promote engagement with key concepts and methods in the domain, but statistical literacy involves the selection of appropriate methods from among competing plausible alternatives, as well as the correct application of the methods, and then reasoning with those methods and their results. A new, developmental, model of statistical literacy that is specific for the scientific practitioner was outlined by Tractenberg (2017-a), based on prior models of statistical and scientific thinking. In this model, there are nine elements to statistical literacy: 1) Define a problem based on critical literature review; 2) Identify or choose – and justify - the measurement system; 3) Design the collection of data; 4) Pilot, analysis and interpretation; 5) Discern “exploratory”, “planned”, and “unplanned” data analysis; 6) Hypothesis generation based on planned & unplanned analyses; 7) Interpretation of results; 8) Drawing and contextualize conclusions; and 9) Communication. These knowledge, skills, and abilities (KSAs) are important for an individual to execute when they are doing scientific work, but these KSAs must also be developed to a sufficient level to permit an individual to assess and consume research articles and arguments that use data and inferences. The nine KSAs that comprise statistical literacy for the practicing scientist each require considerably more cognitive capacity than “engagement with key concepts and methods”. The aim of this paper is to discuss the relationship between purposeful development of these KSAs and Bloom’s taxonomy of educational outcomes (cognitive) so that statistical literacy can be clearly seen as a learnable, improvable skill set that can be introduced in the context of a journal club, among other options, and can continue to be used and developed by individuals and instructors.

Date: 2018-12-01
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:8jn3e_v2

DOI: 10.31219/osf.io/8jn3e_v2

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