One Categorical Variable

CO-4: Distinguish among different measurement scales, choose the appropriate descriptive and inferential statistical methods based on these distinctions, and interpret the results.
Video: One Categorical Variable (4:57)

Related SAS Tutorials

Related SPSS Tutorials

Distribution of One Categorical Variable

LO 4.3: Using appropriate numerical measures and/or visual displays, describe the distribution of a categorical variable in context.

What is your perception of your own body? Do you feel that you are overweight, underweight, or about right?

A random sample of 1,200 U.S. college students were asked this question as part of a larger survey. The following table shows part of the responses:

Student Body Image
student 25 overweight
student 26 about right
student 27 underweight
student 28 about right
student 29 about right

Here is some information that would be interesting to get from these data:

  • What percentage of the sampled students fall into each category?
  • How are students divided across the three body image categories? Are they equally divided? If not, do the percentages follow some other kind of pattern?

There is no way that we can answer these questions by looking at the raw data, which are in the form of a long list of 1,200 responses, and thus not very useful.

Both of these questions will be easily answered once we summarize and look at the distribution of the variable Body Image (i.e., once we summarize how often each of the categories occurs).

Numerical Measures

In order to summarize the distribution of a categorical variable, we first create a table of the different values (categories) the variable takes, how many times each value occurs (count) and, more importantly, how often each value occurs (by converting the counts to percentages).

The result is often called a Frequency Distribution or Frequency Table.

A  Frequency Distribution or Frequency Table is the primary set of numerical measures for one categorical variable.

  • Consists of a table with each category along with the count and percentage for each category.
  • Provides a summary of the distribution for one categorical variable.

Here is the table for our example:

Category Count Percent
About right 855 (855/1200)*100 = 71.3%
Overweight 235 (235/1200)*100 = 19.6%
Underweight 110 (110/1200)*100 = 9.2%
Total n=1200 100%


  1. If you add the percentages in the above table you will get a total of 100.1% (instead of the true value which is, of course, 100%).This can occur whenever rounding has taken place. You should be aware of this possibility when working with real data.If you add the ratios directly as fractions, you will always get exactly 1 (or 100%).

  2. In general, although it might be “less confusing” if we recorded the full values above (71.25% instead of 71.3% and so on), we prefer not to display too many decimal places as this can distract from the conclusions we want to illustrate.We don’t want those who are reading our results to be overwhelmed or distracted by unneeded digits.

Visual or Graphical Displays

In order to visualize the numerical measures we’ve obtained, we need a graphical display.

There are two simple graphical displays for visualizing the distribution of one categorical variable:

  • Pie Charts
  • Bar Charts

Pie Chart

A pie chart of the distribution. Taking up 71.3% of the chart is the "about right" category, which is labeled with "about right (855, 71.3%)". Another 9.2% of the chart os occupied by the section labeled "underweight (110, 9.2%)", and taking up 19.6% of the chart is the area labeled "overweight (235, 19.6%)". In total the three sections fill up the entire pie, so they make up 100% of the chart, which represents the entirety of the data.

Bar Chart

Two bar charts. Since these bar charts can only show one type of unit on the vertical axis, two are required, one to show counts and one to show percentages. The first bar chart shows counts on the vertical axis, from 0 to 900. The horizontal axis has 3 labels under 3 bars. The largest bar is labeled "about right" and is the largest. It extends from the 0 mark on the vertical axis to between the 800 and 900 mark. The second bar is labeled "overweight" and starts at the 0 mark and ends at about the 200 mark. The third bar is labeled "underweight" and starts at the 0 mark and ends between the 100 and 200 mark. The second bar chart is identical to the first one, except the vertical axis has been changed to Percent units, and goes from 0 to 70. The bars are the same as in the first chart.

Note that the pie chart and bar chart are visual representations of the information in the frequency table.

Study the bar charts above and then answer the following question.

Learn By Doing: Bar Charts

Now that we have summarized the distribution of values in the Body Image variable, let’s go back and interpret the results in the context of the questions that we posed. Study the frequency table and graphs and answer the following questions.

Now that we’ve interpreted the results, there are some other interesting questions that arise:

  • Can we reliably generalize our results to the entire population of interest and conclude that a similar distribution across body image categories exists among all U.S. college students? In particular, can we make such a generalization even though our sample consisted of only 1,200 students, which is a very small fraction of the entire population?
  • If we had separated our sample by gender and looked at males and females separately, would we have found a similar distribution across body image categories?

These are the types of questions that we will deal with in future sections of the course.

Recall: Categorical variables take category or label values, and place an individual into one of several groups. Categorical variables are often further classified as either

  • Nominal, when there is no natural ordering among the categories. Common examples would be gender, eye color, or ethnicity.
  • Ordinal, when there is a natural order among the categories, such as, ranking scales or letter grades. However, ordinal variables are categorical and do not provide precise measurements. Differences are not precisely meaningful, for example, if one student scores an A and another a B on an assignment, we cannot say precisely the difference in their scores, only that an A is larger than a B.

Note: For ordinal categorical variables, pie charts are seldom used since the information about the order can be lost in such a display. Be careful that bar charts for ordinal variables display the data in a reasonable order given the scenario.

While both the pie chart and the bar chart help us visualize the distribution of a categorical variable, the pie chart emphasizes how the different categories relate to the whole, and the bar chart emphasizes how the different categories compare with each other.


A variation on the pie chart and bar chart that is very commonly used in the media is the pictogram. Here are two examples:

A bar chart in which the bars have been replaced by rolls of unraveled toilet paper. The chart is titled "How we flush a public toilet" The first bar is labeled "Use shoe, 41%", the second bar is labeled "Act normally 30%", and the last bar is labeled "Paper towel 17%"

Source: USA Today Snapshots and the Impulse Research for Northern Confidential Bathroom survey

A pie chart made out of a slice of cucumber. The cucumber is on a fork, which in turn is over a dinner table. The pie chart is titled "How often are salads eaten (per week)". The pie chart shows 4 sections: Never (3%), Daily (13%), 2 or less (37%), 3-6 times (47%).

Source: Market Facts for the Association of Dressings and Sauces

Beware: Pictograms can be misleading. Consider the following pictogram:

A chart in which three items are represented by the size of a fountain pen. The chart is labeled "No. 1 for the Money with Consumer Services Advertisers" The smallest pen is U.S. News $1,537,617. The second smallest pen is Newsweek $2,698,386. The largest pen is TIME $4,433,879.

This graph is aimed at advertisers deciding where to spend their budgets, and clearly suggests that Time magazine attracts by far the largest amount of advertising spending.

Are the differences really as dramatic as the graph suggests?

If we look carefully at the numbers above the pens, we find that advertisers spend in Time only $4,433,879 / $2,698,386 = 1.64 times more than in Newsweek, and only $4,433,879 / $1,537,617 = 2.88 times more than in U.S. News.

By looking at the pictogram, however, we get the impression that Time is much further ahead. Why?

In order to magnify the picture without distorting it, we must increase both its height and width. As a result, the area of Time’s pen is 1.64 * 1.64 = 2.7 times larger than the Newsweek pen, and 2.88 * 2.88 = 8.3 times larger than the U.S. News pen. Our eyes capture the area of the pens rather than only the height, and so we are misled to think that Time is a bigger winner than it really is.

Let’s Summarize

The distribution of a categorical variable is summarized using:

  • Visual display: pie chart or bar chart, supplemented by
  • Numerical measures: frequency table of category counts and percentages.

A variation on pie charts and bar charts is the pictogram. Pictograms can be misleading, so make sure to use a critical approach when interpreting the information the pictogram is trying to convey.