This “pedagogical reflection” by Joanna Wolfe utilizes “Perelman and Olbrechts-Tyteca’s concept of interpretative level” to elucidate the rhetorical decisions people make when selecting and presenting data and to provide a theoretical foundation for two exercises and a formal assignment Wolfe designed to teach “data visualization” and “writing about data” in communication courses (Wolfe, 2015, pp. 344-345, 348).
According to Wolfe, “interpretative level” is used by Perelman and Olbrechts-Tyteca “to describe the act of choosing between competing, valid interpretations” (Wolfe, 2015, p. 345). In relation to data specifically, Wolfe states the concept can be applied to describe “the choice we make to summarize data on variable x versus variable y” and explains further by emphasizing how people decide whether data are presented as, for example, “averages versus percentages or raw counts” and how those choices have “dramatic consequences for the stories we might tell about data” (Wolfe, 2015, pp. 345-346).
By focusing on interpretative level, Wolfe hopes to address what she perceives as a deficiency by technical communication textbooks to address strategic concerns that would encourage authors to “return to the data to reconsider what data are selected, how they are summarized, and whether they should be synthesized with other data for a more compelling argument” (Wolfe, 2015, p. 345). Although Wolfe praises the communication literature and technical communication textbooks for addressing tactical concerns such as aligning visualization designs with type of data or adjusting visualizations for specific audiences or considering “how to ethically represent data,” she proposes greater involvement with the data to address strategic concerns such as what is the rhetorical purpose and context and which tactics should be used to advance the overall rhetorical strategy (Wolfe, 2015, pp. 348).
In the main body of her paper, Wolfe explains the two exercises and formal assignment she designed to teach students the interpretative level concept and to enable them to practice using it by creating data visualizations from actual data sets (Wolfe, 2015, pp. 348-356). In the first exercise, she demonstrates how deciding on what variable to sort data in a table will determine which “’story or narrative’” is immediately perceived by most viewers (Wolfe, 2015, p. 349) and she explains how to have students practice creating data visualizations to present the “’fairest’” view of Olympics medal data (Wolfe, 2015, pp. 348-251). In the second exercise and in the formal assignment, Wolfe continues adding complexity by increasing the number of analytical points and potential visualization methods students should consider (Wolfe, 2015, pp. (351-355). This increased complexity allows Wolfe to discuss additional methods for visualizing data. She explains, for example, how to consolidate variables using point systems to provide an index score that better summarizes data and how to use stylistic and organizational choices in visualizations to reveal patterns in the data that enable viewers to “derive conclusions” aligned with the authors’ decisions regarding rhetorical strategy (Wolfe, 2015, pp. 353-356).
In conclusion, Wolfe proposes again that communication instruction regarding data visualization should go beyond teaching optimal data visualization tactics by introducing concepts such as interpretative level that encourage students to create rhetorical strategies – and to revisit the data and the analysis and the rhetorical purpose and context – and thereby invent “narratives” that attain those strategies (Wolfe, 2015, p. 357). This, according to Wolfe, will enable students to see data not “as pure, unmodifiable fact,” but “as a series of rhetorical choices” (Wolfe, 2015, pp. 357).