How the Mastery Rubric for Statistical Literacy Can Generate Actionable Evidence about Statistical and Quantitative Learning Outcomes

Rochelle Tractenberg

doi:10.3390/educsci7010003

Abstract

Statistical literacy is essential to an informed citizenry; and two emerging trends highlight a growing need for training that achieves this literacy. The first trend is towards “big” data: while automated analyses can exploit massive amounts of data, the interpretation—and possibly more importantly, the replication—of results are challenging without adequate statistical literacy. The second trend is that science and scientific publishing are struggling with insufficient/inappropriate statistical reasoning in writing, reviewing, and editing. This paper describes a model for statistical literacy (SL) and its development that can support modern scientific practice. An established curriculum development and evaluation tool—the Mastery Rubric—is integrated with a new, developmental, model of statistical literacy that reflects the complexity of reasoning and habits of mind that scientists need to cultivate in order to recognize, choose, and interpret statistical methods. This developmental model provides actionable evidence, and explicit opportunities for consequential assessment that serves students, instructors, developers/reviewers/accreditors of a curriculum, and institutions. By supporting the enrichment, rather than increasing the amount, of statistical training in the basic and life sciences, this approach supports curriculum development, evaluation, and delivery to promote statistical literacy for students and a collective quantitative proficiency more broadly.

Highlights

Statistical literacy (SL) is widely described as important for full social participation
Undergraduate statistical literacy is fundamentally different from that required for applied science and for doctoral level work, but it is not expertise in statistics that is targeted with the Mastery Rubric for Statistical Literacy (MR-SL)—it is expertise, or movement towards it, in this particular type of literacy that is targeted
Many PhD science programs require a single statistics course, and this may suffice for undergraduates, statistical literacy to support responsible stewardship of a scientific discipline differs fundamentally from that of undergraduates

Summary

Introduction

Statistical literacy (SL) is widely described as important for full social participation (see [1]; elementary curricula, e.g., [2,3]; higher education and beyond, e.g., [4,5,6]). Empirical research relies on statistical methods, and statistics is a wide, dynamic field perpetually propelled by new and improved methods. This far outstrips the capacities of other fields to fully adapt to these innovations, much less to incorporate all “relevant” methods in their own PhD curricula.

Methods

Results

Discussion

Conclusion