Aggregating, Analyzing, and Visualizing Student Survey Data to Improve Targeted Local Support - Module 2
The case uses anonymous socio-economic survey data from middle- and high-school students enrolled in Fe y Alegría schools in Bolivia (FyA:B) to teach data aggregation, data cleaning, and data visualization to undergraduate and/or graduate business school students. The content of the survey data relates to third world educational and economic realities and therefore increases awareness that leads to service for the common good. While anonymous, the survey data provides context on the daily life of disadvantaged communities served by FyA:B schools and can assist Jesuit business schools in aligning with the Inspirational Paradigm by focusing on the plight of the underserved, by providing actionable lessons and assignments, by stimulating critical reflection, and by offering opportunities to engage with FyA:B’s work.
The case covers basic data combination, analysis, and visualization skills and was designed for up to twelve-week course sequence intentionally developed to allow flexibility in breadth and depth of coverage. It focuses on fostering familiarity with commonly used software tools (Excel, Alteryx, and Tableau) to develop data analysis skills that have recently been highly valued in the labor market for current STEM and Business college graduates. The case does so by examining anonymized surveys conducted to better understand student socio-economic conditions in FyA:B school communities across different regions over a period of several years. The surveys had either 22 and 28 questions and asked questions regarding students’ access to basic needs (e.g., water, meals) as well as social environment (e.g., living conditions, parent education and occupation). The original objective of the surveys was to help FyA:B schools identify students in most need so targeted tailored support could be made available to them. The case is composed of three modules that can be used in their entirety or only in part, so at the instructor’s discretion it is possible to teach the material in less time (either by eliminating content or by compressing delivery).
Module 2: Union of several schools, renaming headers (v-lookup and dynamic renaming), applying data translation formulas, and basic visualization This module extends the one-school aggregation to multi-school aggregation using surveys that are similar but not exactly the same. The module focuses on various tasks that can be achieved with either Excel or Alteryx while highlighting advantages of each (e.g., Copying and Pasting vs. Unioning and V-Lookup vs. Dynamic Renaming). The second module can be taught in three to five weeks depending on the desired level of immersion and reflection.