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Effective Visualization of Categorical Data: Bar vs. Circle Graphs

Explore the differences between bar and circle graphs for displaying categorical data effectively, understanding conditional and marginal distributions, and analyzing associations between variables. Which is the best graph for your data?

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Effective Visualization of Categorical Data: Bar vs. Circle Graphs

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  1. Analyzing Categorical Data Categorical data is data divided in categories and each category has a corresponding value Ways to display categorical data: Bar GraphCircle GraphTwo Way Table

  2. Graphs: Good and Bad Bar graphs compare several quantities by comparing the heights of bars that represent those quantities. Our eyes, however, react to the area of the bars as well as to their height. Why do the bars on the two graphs look so relatively different? Looks 10 times taller than PC

  3. Which graph is best? Portable music players are popular, but not equally with people of all ages. Below is a chart of the percent of people within each group that own a portable music player (non-phone), according to a survey of 1112 randomly selected people. Which type of categorical graph would be most appropriate for this data?

  4. Two Way Tables These allow for analysis of the distribution of a single categorical variable Example: A survey of 4826 randomly selected young adults (19 – 25 years old) asked “What do you think the chances are that you will have MORE than a middle class income?” The table below gives their responses. What percent of the people believed a 50-50 chance? What percent of the people were almost certain? The totals are called MARGINAL DISTRIBUTIONS.

  5. Conditional Distributions describe the value of a variable based upon the value of another variable. What percentage of females claimed there is almost no chance? What percent of those claiming a good chance are male?

  6. Two Way Tables can be displayed in graphs as well. Double Bar Graph Stacked Bar Graph

  7. Two Way Tables also allows us to determine if there is an association between two variables. This means knowing the value of one variable will help you predict the value of another. Is there an association between gender and opinion about financial status?

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