The graphical representation of quantitative information is not a modern development, but rather it can be traced back to the earliest map-making and, later, thematic cartography and statistical graphics (Friendly, 2008). The early 19th century witnessed the invention of all major forms of statistical graphics, including the ever so popular pie and bar charts, histograms, line graphs, and scatterplots. At this time, data from a wide variety of domains (e.g., economic, social, medical, physical) began to be depicted, and a wide range of novel techniques were used to facilitate data representation. At the same time, graphical analyses of natural and physical phenomena made regular appearances in scientific publications. In the second half of the 19th century, there was a rapid growth in the visualization of data: the importance of numerical information for public policy, industry, and health was acknowledged, and the various applications of statistical theory and methods made it easier to make sense of large bodies of data. This period has been referred to “the Golden Age” of data visualization (Friendly, 2008, pp. 12–13).

Another historically critical period of the development of data visualization is between 1950 and 1975 (Friendly, 2008). In this period, data analysis began being recognized as a distinct branch of statistics by the international research community and significant advances were made in the area of computer processing of statistical data, interactive statistical applications, and digital graphic technologies. Since the mid-1970s, data visualization has blossomed into a vibrant multi-disciplinary research area. It features characteristics such as highly interactive statistical computing systems, advanced visualizations of high-dimensional data, and substantially increased attention to the cognitive and perceptual aspects of data display.

Data representations and visualizations have also become commonplace in the applied work of a variety of professions. Scientists, for example, use data visualizations to make sense of trends within their research that employs mathematical and statistical models of phenomena and make such results understandable by others. Engineers use data representations to monitor environmental, commercial, and industrial processes. Historians and journalists also utilize data representations and visualizations to communicate information from a myriad of sources, including textual data. Finally, more recently, individuals – even students – have begun to use data representations and visualizations to understand aspects of their lives, such as their wellness and finances (Lee, Choe, Isenberg, Marriott, & Stasko, 2020). Not left behind the data revolution are educational researchers, who use data representations and visualizations, which we use synonymously in this article, for many of the same reasons as other professionals and non-professionals – to understand and communicate results effectively.

While many engage with data representations and visualizations, a focus on the effectiveness of their design has often been ignored (Wilkinson, 2005). However, after a period where data representations and visualizations were seen by statisticians as “a minor subfield and are not well-integrated with larger themes of modeling and inference” (Gelman & Unwin, 2013, p. 1), many professionals are beginning to take representation and visualization seriously. This is evidenced by the recent theoretical and practical work that is being done by the likes of Healy (2018), Wickham (2016), and Wilkinson (2005). Moreover, there is research and recent work in the broader fields of computer science, statistics, and sociology, to name a few, that can inform how we, as educational researchers, go about creating data representations and visualizations effectively. Finally, as we begin considering effective ways to represent and visualize data, it is important to consider findings from educational, psychological, and developmental research on how people interpret data representations and visualizations as we make related decisions. Thus, there is, presently, a greater focus on the effectiveness of data representations and visualizations, a focus which we aim to highlight and demonstrate through this article.