Creating data accountable graphics: Changing how you display your data changes the way your data are understood
Dr. Jenifer Larson-Hall, The University of Kitakyushu (Japan)
This hands-on workshop will expose you to several types of data displays available for both quantitative and qualitative research results and equip you with the knowledge to choose the most effective display for different data sets. You will use the statistical program R to fashion your own high-quality, attractive graphics that can display both the individual data behind your results as well as show the overall trends of your data as a whole, producing what I call ‘data-accountable’ graphics. Participants who register for the workshop by the early bird deadline will be invited to complete a brief survey to identify their interests and background, and will also have the option of submitting their own data and/or graphics , which I will refer to during the workshop. However, neither the survey completion nor the submission of data/graphics are necessary to benefit from the workshop: All participants will take away the ability to showcase their research findings in novel, effective ways.
Jenifer Larson-Hall is an Associate Professor at Kitakyushu University, Japan. In "Moving Beyond the Bar Plot and the Line Graph to Create Informative and Attractive Graphics," The Modern Language Journal, 2017, https://doi.org/10.1111/modl.12386 , she examined the role of graphics to better understand and support statistical thinking. Graphics also play an important role in her statistical textbooks: A Guide to Doing Statistics in Second Language Research Using SPSS (2010) and A Guide to Doing Statistics in Second Language Research Using SPSS & R (2016) (companion website, including additional statistical tests, at http://www.routledgetextbooks.com/textbooks/9781138024571/. Dr. Larson-Hall is working to improve informational graphics reporting data in Applied Linguistics, especially data-accountable graphics (Larson-Hall, 2017; Larson-Hall & Plonsky, 2015) which show all of the data points behind the statistical reasoning while also showing mean and median lines.