To produce the plots, I used the Python implementation of Seaborn in a Google Colab Notebook. The data came from my own published version of the SCS1 concept inventor by Parker, Guzdial, and Engleman. I took the data from Qualtrics and reduced it a bit in SPSS by IBM. Next, I put the reduced data into a comma-separated values file (CSV). Then I wrote some Python code to load the data into pandas dataframe which is a very handy container that allows for complex data analysis techniques. Seaborn is cited quite often in literature and is easily one of the most popular data science packages in use today. I would be remiss if I didn’t also mention that Seaborn used the wildly successful matplotlib graphics package in Python.
For the infographic itself, I used one of the infographic templates inside Canva. The tool itself was fairly easy to use. I’ve worked with desktop publishing software in many forms and Canva is a very helpful web-based graphics application
Some other visualizations I created in python are below.