Inside the Venn Diagram
The power of a Venn Diagram comes from its ability to bring data to life. The diagram has an ability to take two or more variables and show a relationship between them. Providing a beautiful visual of how things connect. However, once the connection is made the diagram shows an inability to further analyze.
While the diagram had been used previously in history, the first documentation of it came from John Venn in the 1880’s. Falling into the logic class of reasoning. Its original intent was to illustrate simple relationships.
The Venn starts with two sets of data called elements with independent factors. Both sets are categorized separately in spheres. Then the elements are merged until there is an overlap of both spheres. This overlap of both sides is called the intersection and the purpose of the Venn Diagram.
For example, we can figure out the greatest baseball player of all time. We can use three elements to base this on. Set A would be baseball members of the Hall of Fame, which number 340 at the time of this writing. Second, for element B we would evaluate the number individuals that hit over 500 home runs, which number 28 at the time of this writing. Lastly, for element C we would evaluate who had over 3,000 base hits, which at the time of this writing was 32 players. All three independent elements show us three important data points to the greatest player of all time. The beauty of the Venn Diagram is that it will also show us the 3 players that are in the Hall of Fame, with 500 home runs, and 3,000 hits.
The Venn Diagram is an amazing tool especially for the visual learner. It illustrates complex topics simply, especially when there is a concept overlap. It additionally places the viewer into a higher order of thinking.
However, the Venn does have its shortfalls. The tool struggles with new ideas and concepts. It has a limited capability to compare and contrast. Most importantly, it fails to further understand the intersection in a second level of analysis.
This paper’s focus is on that inside decision-making analysis. To propose a second step to understand the intersection. To extend the decision making by ranking the variables in the intersection to further explore them.
Under this proposal a researcher would identify additional variables. These could possibly be another element for the intersection, but these would not be binary determinant. Using our baseball example, we would also want to factor in runs scored and runs batted in. Instead of making this a cutoff element we would use it to rank our intersection.
If we continue with our baseball example, we have identified three players that are in the Hall of Fame, have hit 500 home runs, and have hit 3,000 hits. But how do we know who the best player was of these three Hank Aaron, Willie Mays, or Eddie Murray? We can then factor in a ranking of additional variables to further analyze the intersection. In this example we can rank these three by the number of runs they scored and the number of runs they produced or runs batted in (RBI). This enables us to rank Hank Arron as the greatest baseball player of all time. While the elements used here are subjective it represents an approach to further analysis.
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