Infographics is a visualization that combines art and minimal text to make data easily understandable at a quick glance (Rouse 2012).
Tableau public tutorials series#
Méndez-Carbajo ( 2015) and Méndez-Carbajo, Taylor, and Bayles ( 2017) provide several assignments (using prepackaged data from the FRED online database) illustrating theoretical macroeconomic relationships with time series data. FRED specializes in the visualization of time series data.
Louis 2019) combines charting and mapping tools with an online database of economic time series. Peterson ( 2000) provides four assignments, using data prepackaged for GIS, to create thematic maps visualizing geo-spatial economic relationships for topics in economic development.
Tableau public tutorials software#
GIS (Geographic Information Systems) is a software that has the specific purpose of linking geographic information with descriptive information (ESRI 2012, 6). The four visualization tools that have been explored in the economics of education literature are GIS, FRED, Infographics, and Excel. In our search of the economic education literature, we uncovered four such exercises, as discussed herein, none of which uses Tableau. However, examples of modules published in the economic education literature with exercises aimed at developing data literacy skills for undergraduate economics courses are relatively few, although growing. Data visualization helps the researcher identify relationships in data and enhances a presentation in the same way as a thousand words while taking up less space (Hennessey 2014). By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data” ( 2019, online). 1ĭata visualization is a tool of data literacy: “Data visualization is the graphical representation of information and data. Common to the department efforts is a focus on developing data literacy and investing in infrastructure support from data librarians as well as IT support specialists in software usage (e.g., Stata and GIS). A recent symposium section of the Journal of Economic Education highlights efforts in five top economics departments that have the goal of promoting economic literacy and a culture of undergraduate research (Hoyt and McGoldrick 2017a). The penultimate goal defines what data literacy is, and is clearly foundational to the last goal of presenting the findings. They identify five goals of student research: (1) develop a worthwhile research question (2) connect it to the literature (3) formulate a testable hypothesis (4) collect, clean, and use appropriate tools to analyze data to test the hypothesis and (5) present the findings. Hoyt and McGoldrick ( 2017b) summarize what the literature emphasized that “doing economics” was at that time, using analytical and presentation tools that students will be using after graduation. A key factor in the orientation toward developing empirical quantitative methods is recognition of a greater demand by employers for these skills (Angrist and Pischke 2017 Kassens 2019 Marshall and Underwood 2020).ĭata literacy is emerging as an important component of “doing economics” (Halliday 2019).
By 2018, economics degrees classified as STEM-eligible (econometrics and quantitative) comprised 13.2 percent of all undergraduate economics degrees conferred, up from just 1.2 percent in 2012 (Marshall and Underwood 2020). Similarly, undergraduate departments are turning toward increasing instruction in empirical and quantitative methods. The pedagogy of undergraduate econometrics is moving, albeit slowly, from the “Stone Age” emphasis on statistical efficiency and functional form to the “Computer Age” of causal interpretation and empirical examples (Griffith and McFall 2013 Angrist and Pischke 2017). “Doing economics” in undergraduate economics increasingly includes working with data. ( 1991) and Hansen ( 2001) these phrases have become the mantra for writers on economic education as Hoyt and McGoldrick document. Since the 1990s, the phrases “learning by doing,” and “training to think like an economist” appear in seminal articles by Siegfried et al. Before the 1990s, economic educators were concerned with incorporating emerging technologies into instruction. Hoyt and McGoldrick ( 2019) survey the five decades starting from the 1970s and single out the decline in the number of economics majors in the 1990s as a transition point in the economic education literature.