2.2 Displaying and Describing Categorical Data

Descriptive Statistics for Categorical Data

is typically more straightforward to work with.  Recall descriptive statistics consists of visual and numerical methods.  We usually start with visual methods and then move into numerical.

Graphical Methods for Categorical Data

Below are tables comparing the number of part-time and full-time students at De Anza College and Foothill College enrolled for the spring 2010 quarter. The tables display counts (frequencies) and percentages or proportions (relative frequencies). The percent columns make comparing the same categories in the colleges easier. Displaying percentages along with the numbers is often helpful, but it is particularly important when comparing sets of data that do not have the same totals, such as the total enrollments for both colleges in this example. Notice how much larger the percentage for part-time students at Foothill College is compared to De Anza College.

Figure 2.6: Full and Part Time Students
De Anza College Foothill College
Number Percent Number Percent
Full-time 9,200 40.9% Full-time 4,059 28.6%
Part-time 13,296 59.1% Part-time 10,124 71.4%
Total 22,496 100% Total 14,183 100%

Tables are a good way of organizing and displaying data. But graphs can be even more helpful in understanding the data. There are no strict rules concerning which graphs to use. Two graphs that are used to display categorical data are pie charts and bar graphs.

Pie Charts 

In a pie chart, categories of data are represented by wedges in a circle and are proportional in size to the percent of individuals in each category.  Suppose a statistics professor collects information about the classification of her students as freshmen, sophomores, juniors, or seniors. The data she collects are summarized in the pie chart below.

Pie chart showing the class classification of statistics students. The chart has 4 sections labeled Freshman, Sophomore, Junior, Senior with associated descending pie slice sizes.
Figure 2.7: Classification of Statistics Students

Bar Graphs

Bar graphs consist of bars that are separated from each other.  The length of the bar for each category is proportional to the number or percent of individuals in each category.  The bars can be rectangles or they can be rectangular boxes (used in three-dimensional plots), and they can be vertical or horizontal. The bar graph shown in the figure below has age groups represented on the x-axis and proportions on the y-axis.

By the end of 2011, Facebook had over 146 million users in the United States. The figure below shows three age groups, the number of users in each age group, and the proportion (%) of users in each age group. Construct a bar graph using this data.

Figure 2.8: Facebook Users
Age groups Number of Facebook users Proportion (%) of Facebook users
13–25 65,082,280 45%
26–44 53,300,200 36%
45–64 27,885,100 19%
Bar graph that matches the supplied data. The x-axis shows age groups (13-25, 26-44, 45-64), and the y-axis shows the proportion (%) of Facebook users ranging from 0 to 50.
Figure 2.9: Facebook Users (Bar Graph)

Pie vs. Bar Charts

It is a good idea to look at a variety of graphs to see which is the most helpful in displaying the data. We might make different choices of what we think is the “best” graph depending on the data and the context. Our choice also depends on what we are using the data for.  Look at the following plots (pie or bar) and think about which you think displays the comparisons better:

Two pie charts. Left pie chart labeled Virginia Tech separated into 2 pie slices: Part time (7.8%) and Full time (92.2%). Right pie chart labeled Northern Virginia Community College separated into 2 pie slices: Part time (65.4%) and Full time (34.6%).
Figure 2.10: Full-time and Part-time Students at Virginia Tech and NVCC
Bar graph of 'Student Status'. Y axis range from 0 to 14,000, X axis shows: De Anza full time (9200), De Anza part time (13296), Foothill full time (4059), Foothill part time (10124).
Figure 2.11: Full-time and Part-time Students at Virginia Tech and NVCC (Bar Graph)

Percentages That Add to More (or Less) than 100%

Sometimes percentages add up to be more than 100% (or less than 100%). In the graph, the percentages add to more than 100% because students can be in more than one category. A bar graph is appropriate to compare the relative size of the categories. A pie chart cannot be used. It also could not be used if the percentages added to less than 100%.
Figure 2.12: De Anza College Data
Characteristic/Category Percent
Full-Time Students 40.9%
Students who intend to transfer to a 4-year educational institution 48.6%
Students under age 25 61.0%
TOTAL 150.5%
Bar graph with Y axis ranging from 0% to 100% by 20%. X axis values: Under age 25 (61%), Intend to transfer (48.6%), full time (40.9%), all students (100%).
Figure 2.13: De Anza College Bar Graph

Omitting Categories/Missing Data

The table displays Ethnicity of Students but is missing the “Other/Unknown” category. This category contains people who did not feel they fit into any of the ethnicity categories or declined to respond. Notice that the frequencies do not add up to the total number of students. In this situation, create a bar graph and not a pie chart.

Figure 2.14: Ethnicity of Students at De Anza College
Frequency Percent
Asian 8,794 36.1%
Black 1,412 5.8%
Filipino 1,298 5.3%
Hispanic 4,180 17.1%
Native American 146 0.6%
Pacific Islander 236 1.0%
White 5,978 24.5%
TOTAL 22,044 out of 24,382 90.4% out of 100%
Bar graph with Y axis ranging from 0% to 40% by 5%. X axis values: Asian (26.1%), Black (5.8%), Filipino (5.3%), Hispanic (17.1%), Native American (0.6%), Pacific Islander (1%), White (24.5%).
Figure 2.15: Ethnicity of Students at De Anza College (Bar Graph)

The following graph is the same as the previous graph but the “Other/Unknown” percent (9.6%) has been included. The “Other/Unknown” category is large compared to some of the other categories (Native American, 0.6%, Pacific Islander 1.0%). This is important to know when we think about what the data are telling us.

Bar Graph with Other/Unknown Category

Bar graph with Y axis ranging from 0% to 40% by 5%. X axis values: Asian (26.1%), Black (5.8%), Filipino (5.3%), Hispanic (17.1%), Native American (0.6%), Pacific Islander (1%), White (24.5%), Other/Unknown (9.6%).
Figure 2.16: Ethnicity of Students at De Anza College (Bar Graph with ‘Other’ Category)
This particular bar graph could be difficult to understand visually at first glance.  A Pareto chart consists of bars that are sorted into order by category size (largest to smallest). This Pareto chart has the bars sorted from largest to smallest and is easier to read and interpret.
Bar chart including the same values as Figure 2.17, however this figure is sorted from highest to lowest, left to right, beginning with Asian (26.1%) and ending with Native American (0.6%).
Figure 2.17: Ethnicity of Students at De Anza College (Bar Graph with ‘Other’ Category)

Pie Charts: No Missing Data

The following pie charts have the “Other/Unknown” category included (since the percentages must add to 100%). The second chart below is organized by the size of each wedge, which makes it a more visually informative graph than the unsorted, alphabetical graph in the first chart below.

Two pie charts side by side both titled 'Ethnicity of Students'. Left: Asian (36.1%), Black (5.8%), Filipino (5.3%), Hispanic (17.1%), Native American (0.6%), Pacific Islander (1%), White (24.5%), Other (9.6%). Right pie chart includes the same values but arranged in descending order starting with Asian (36.1%) and ending with Native American (0.6%).
Figure 2.18: Pie Charts With No Missing Data

Example

The columns in the figure below contain: the race or ethnicity of students in U.S. Public Schools for the class of 2011, percentages for the Advanced Placement examine population for that class, and percentages for the overall student population. Create a bar graph with the student race or ethnicity (qualitative data) on the x-axis, and the Advanced Placement examinee population percentages on the y-axis.

Figure 2.19: AP Student Population
Race/Ethnicity AP Examinee Population Overall Student Population
1 = Asian, Asian American or Pacific Islander 10.3% 5.7%
2 = Black or African American 9.0% 14.7%
3 = Hispanic or Latino 17.0% 17.6%
4 = American Indian or Alaska Native 0.6% 1.1%
5 = White 57.1% 59.2%
6 = Not reported/other 6.0% 1.7%

Solution:

Bar graph that matches the supplied data. The x-axis shows race and ethnicity with groups 1-6, and the y-axis shows the percentages of AP examinees ranging from 0.6 to 57.1.
Figure 2.20: AP Student Population (Bar Graph)

 

Your turn!

Park city is broken down into six voting districts. The table shows the percent of the total registered voter population that lives in each district as well as the percent total of the entire population that lives in each district. Construct a bar graph that shows the registered voter population by district.

Figure 2.21: Registered Voter Population by District
District Registered voter population Overall city population
1 15.5% 19.4%
2 12.2% 15.6%
3 9.8% 9.0%
4 17.4% 18.5%
5 22.8% 20.7%
6 22.3% 16.8%

Construct a bar graph that shows the registered voter population by district.

Describing Categorical Data

After we have displayed the data visually, we then want to follow up by describing it with numerical measures.  Since Categorical Data does not lend itself to mathematical calculations by nature there are not many numerical descriptors we can use to describe it.  However, we can describe a categorical distribution’s “typical value” with the , and can also note its level of .  

Mode

The Mode of a dataset is the most frequently occurring value. There can be more than one mode in a data set as long as those values have the same frequency and that frequency is the highest. A data set with two modes is called bimodal, three modes – trimodal, multiple modes – multimodal, etc.  In most cases the mode can easily be found as the largest piece of a pie chart, or largest bar in a bar chart.  Looking at some previous examples:

Pie chart showing the class classification of statistics students. The chart has 4 sections labeled Freshman, Sophomore, Junior, Senior with associated descending pie slice sizes.
Figure 2.7 (repeat): Classification of Statistics Students
Bar chart including the same values as Figure 2.17, however this figure is sorted from highest to lowest, left to right, beginning with Asian (26.1%) and ending with Native American (0.6%).
Figure 2.17 (repeat): Ethnicity of Students at De Anza College (Bar Graph with ‘Other’ Category)

The mode of the class of Statistics students is obviously Freshman.  If any doubt remains a Pareto chart makes identifying the mode trivial, which is Asian in the previous example.

Variability

The best way to gauge variability in categorical data is by thinking about it as diversity.  Although we will not calculate a numerical measure here, we can note it visually.  A variable that has observations spread out fairly evenly over all categories shows high variability, while a variable where most observations are only in one or a handful of categories displays low variability. Consider the level of variability in the two pie charts below.

Example

Consider the level of variability in the two pie charts below. Which college has more variability?

Two pie charts. Left pie chart labeled Virginia Tech separated into 2 pie slices: Part time (7.8%) and Full time (92.2%). Right pie chart labeled Northern Virginia Community College separated into 2 pie slices: Part time (65.4%) and Full time (34.6%).
Figure 2.10 (repeat): Full-time and Part-time Students at Virginia Tech and NVCC

 

Your turn!

Let’s consider the variability in the following bar charts. Which bar chart shows greater variability?

Two bar graphs side by side. Left: titled 'A' with 'Proportion (%) on the y axis and 'Birthdays in each season' on the x axis. X axis includes: Spring (24%), Summer (26%), Autumn (31%), Winter (18%). Right: titled 'B' with 'Percent of AP examinees' on the y axis and 'Race/Ethnicity' on the x axis. X axis values: 1 (10.3), 2 (9.0), 3 (17.0), 4 (0.6), 5 (57.1), 6 (6.0).
Figure 2.22: Variability Comparisons

 

Image References

Figure 2.7: Kindred Grey (2020). “Classification of Statistics Students.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Classification_of_Statistics_Students.png

Figure 2.9: Kindred Grey (2020). “Ages and proportions.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Ages_and_proportions.png

Figure 2.10: Kindred Grey (2020). “Virginia Tech and NVCC stats.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Virginia_Tech_and_NVCC_stats.png

Figure 2.11: Kindred Grey (2020). “VT and NVCC chart.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:VT_and_NVCC_chart.png

Figure 2.13: Kindred Grey (2020). “Figure 2.13.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.13.png

Figure 2.15: Kindred Grey (2020). “Figure 2.15.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.15.png

Figure 2.16: Kindred Grey (2020). “Figure 2.16.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.16.png

Figure 2.17: Kindred Grey (2020). “Figure 2.17.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.17.png

Figure 2.18: Kindred Grey (2020). “Figure 2.18.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.18.png

Figure 2.20: Kindred Grey (2020). “Figure 2.20.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.20.png

Figure 2.22: Kindred Grey (2020). “Figure 2.22.” CC BY-SA 4.0. Retrieved from https://commons.wikimedia.org/wiki/File:Figure_2.22.png

License

Icon for the Creative Commons Attribution-ShareAlike 4.0 International License

Significant Statistics by John Morgan Russell, OpenStaxCollege, OpenIntro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.

Share This Book