Computational Skills for Multivariable Thinking in Introductory Statistics. (1st January 2021)

• Record Type: Journal Article
• Authors: Adams, Bryan; Baller, Daniel; Jonas, Bryan; Joseph, Anny-Claude; Cummiskey, Kevin
• Is Part Of: Journal of Statistics and Data Science Education. Volume 29(2021)Supplement 1
• Page Start: S123
• Page End: S131
• Publication Date: 2021-01-01
• Subjects: Classroom activity -- COVID-19 -- Censorship -- Statistical computing
• DOI: 10.1080/10691898.2020.1852139
• Languages: English
• ISSNs: 2693-9169

Abstract

ABSTRACT: Since the publishing of Nolan and Temple Lang's "Computing in the Statistics Curriculum" in 2010, the American Statistical Association issued new recommendations in the revised GAISE college report. To reflect modern practice and technologies, they emphasize giving students experience with multivariable thinking. Students develop multivariable thinking when they analyze real data in the context of investigating research questions of interest, which typically involve complex relationships between many variables. Proficiency in a statistical programming language facilitates the development of multivariable thinking by giving students tools to investigate complex data on their own. However, learning a programming language in an introductory course is difficult for many students. In this article, we recommend a set of computational skills for introductory courses, demonstrate them using R tidyverse, and describe a classroom activity to develop computational skills and multivariable thinking. We provide a tidyverse tutorial for introductory students, our course guide, and classroom activities. Supplementary materials for this article are available online at https://github.com/bryaneadams/Computational-Skills-for-Multivariable-Thinking-in-Introductory-Statistics .