Visualizing multivariate data: graphs that tell stories

STATISTICS EDUCATION RESEARCH JOURNAL

Statistical literacy involves engagement with the data one encounters. New forms of data and new ways to engage with data – notably via interactive data visualisations – are emerging. Some of the skills required to work effectively with these new visualisation tools are described. We argue that interactive data visualisations will have as profound an effect on statistical literacy as the introduction of statistics packages had on statistics in social science in the 1960s. Current conceptualisations of statistical literacy are too passive, lacking the exploration part in data analysis. Statistical literacy should be conceived of as empowerment to engage effectively with evidence, and educators should seek to move students along a pathway from using interactive data visualisations to building them and interpreting what they see. First published May 2017 at Statistics Education Research Journal Archives

Statistics Education for Progress: Youth and Official Statistics Proceedings IASE 2013 Satellite Conference, 2013

There has been a great deal of concern in recent times about the capacity of social science students at all levels to cope with the demands of quantitative methods in the curriculum. The Nuffield Foundation funded a project Reasoning from Evidence to produce some data visualisations and associated curriculum materials to support the teaching of social science at Advanced-level (ages 16– 19 in the UK), using data sets relevant to the Sociology curriculum but which have usefulness across other subject areas also. Social sciences deal routinely with contexts in which the population under consideration is not homogenous. The data used is often presented in aggregated form which disguises the characteristics of the subgroups – whether these are by ethnicity, age, socio-economic status, region or some other categorisation. This paper reports on the development of materials using data on health and on the UK public disorder of August 2011. We report on further development of data visualisa...

MedieKultur: Journal of media and communication research

Educational Studies in Mathematics, 2021

Data visualizations have proliferated throughout the COVID-19 pandemic to communicate information about the crisis and influence policy development and individual decision-making. In invoking exponential growth, mathematical modelling, statistical analysis, and the like, these data visualizations invite opportunities for mathematics teaching and learning. Yet data visualizations are social texts, authored from specific points of view, that narrate particular, and often consequential, stories. Their fundamental reliance on quantification and mathematics cements their social positioning as supposedly objective, reliable, and neutral. The reading of any data visualization demands unpacking the role of mathematics, including how data and variables have been formatted and how relationships are framed to narrate stories from particular points of view. We present an approach to a critical reading of data visualizations for the context of mathematics education that draws on three interrelated concepts: mathematical formatting (what gets quantified, measured, and how), framing (how variables are related and through what kind of data visualization), and narrating (which stories the data visualization tells, its potential impacts and limits). This approach to reading data visualisations includes a process of reimagining through reformatting, reframing and renarrating. We illustrate this approach and these three concepts using data visualizations published in the New York Times in 2020 about COVID-19. We offer a set of possible questions to guide a critical reading of data visualizations, beyond this set of examples.

The paper examines the burgeoning practice of visualizing data. It begins with a brief overview of this broad field and the nature of the practice throughout history. The focus is on computational interactive visualizations and the ways in which technology has given way to an expansive and expanding practice mainly centered on current issues.

The American Statistician, 2015

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into 'pictures', and develop interactive visualizations with, e.g., the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, e.g., Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education.

2016

Proceedings of the National Academy of Sciences

In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical ...

College Teaching, 2018

As the collection and dissemination of "big data" become easier, we are exposed to increasing numbers of visuals, or infographics, that convey this data. Students are likely to interact with infographics through news, advertising, and social media platforms, but they may not have the skills in quantitative reasoning or visual literacy to effectively understand how data is employed or framed to tell particular stories. Teaching students about the tools, available data, and communication styles that infographics offer early in their coursework can provide them a basis on which to better develop a variety of analytic and communication tools throughout additional courses in their major field of study. Through the process of learning to locate, evaluate, and use data in infographics, students also become better consumers of data visualization. A series of scaffolded assignments that help accomplish this are detailed here.