Computational Skills for Multivariable Thinking in Introductory Statistics

• Main Authors: Bryan Adams, Daniel Baller, Bryan Jonas, Anny-Claude Joseph, Kevin Cummiskey
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-11-01
• Series: Journal of Statistics Education
• Subjects: data visualization; statistics education; statistical computing; classroom activities; confounding; social media censorship; covid-19
• Online Access: http://dx.doi.org/10.1080/10691898.2020.1852139

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 paper, we recommend a set of computational skills for introductory courses, demonstrate them using , 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.