# Categorical Data Examples and Definition

In the field of statistics and data management, it can be given a huge list of categorical data examples and applications.

Data, in scientific meaning, is a set of information gathered for a purpose. Data is typically divided into two different types: categorical (widely known as qualitative data) and numerical (quantitative).

• What is categorical data? Definition and key characteristics.
• List of 22 examples of categorical data.
• Categorical data vs numerical data.
• Infographic in PDF
Let's define it:

As you might guess, categorical data is data that is divided into groups or categories. These categories are based on qualitative characteristics such as gender and colors or something else that doesn’t have a number associated with it.

This doesn’t mean that categorical data cannot have numerical values.

In fact, categorical data often takes numerical values, but those numbers don’t have any mathematical meaning. They just represent the number of items in each group. For example 12 blondes in a class.

This makes it possible to do categorical data analysis and different manipulations, particularly in a spreadsheet application.

There is no order to categorical values and variables. To put it in another way, they aren’t ranked from highest to lowest.

For example, there is no order to the categories of blue, brown and green eyes.

How to display categorical variables graphically?

Bar charts and pie charts are great tools for comparing two or more categorical values against each other. They just represent the number of things in a category.

For example, if you want to display the number of workers in a company, the outcomes can be presented on a pie chart or on a bar graph.

Graphical categorical data examples:

Survey on “What Motivates Employees to Work Better?”

Before creating a pie or bar chart, you should check if data are in counts or percentages. To make a graphical display of categorical data, it is a necessary condition.

Analysis of categorical data very often includes data tables. The values are represented as a two-way table or contingency table by counting the number of items that are into each category.

Here is an example of a categorical data two-way table for a group of 50 people.

The table shows the results of the groups formed by counting the hair and eye color of each person.

Two-way and contingency tables are great tools for seeing how two categorical variables are related.

The table represents the counts or percentages of persons who belong to a group for two or more quantitative variables. It makes easier to find different relationships between the data.

Let’s sum the key characteristics of categorical data we learned above:

• Categorical data is divided into groups or categories.
• The categories are based on qualitative characteristics.
• There is no order to categorical values and variables.
• Categorical data can take numerical values, but those numbers don’t have any mathematical meaning.
• Categorical data is displayed graphically by bar charts and pie charts.

When it comes to categorical data examples, it can be given a wide range of examples. In our previous post nominal vs ordinal data, we provided a lot of examples of nominal variables (nominal data is the main type of categorical data).

Examples of categorical data:

• Gender (Male, Female)
• Brand of soaps (Dove, Olay…)
• Hair color (Blonde, Brunette, Brown, Red, etc.)
• Survey on a topic “Do you have children?” (Yes or No)
• Motives for employees to work better: (Peer Motivation, To Be Recognized, Opportunities for Professional Growth, Work Culture, the Feeling To Be Involved and etc.)
• Motives for traveling (Leisure, Business Travel, To Visit Friends and etc.)
• Checking account location (California, Texas, Colorado…)
• Educational level: (Associate’s degree, Bachelor’s degree, Master’s degree, Doctoral degree and etc.)
• Age group (under 12 years old, 12-17 years old, 18-24 years old, 25-34 years old, 35-44 years old and etc.)
• Ethnicity (Hispanic, African American, Native American, Asian, Other)
• Eye color (Green, Blue, Brown, Black)
• Household Composition (Single, Married, Widowed, Divorced)
• Employment status (Employed for wages, Self-employed, A homemaker, A student, Retired and etc.)
• Film Genres (Action, Adventure, Comedy, Crime, Mystery, Drama, Historical and etc)
• Home country (Canada, USA, Australia, India, Germany).
• Car color (Red, Green, Grey, Black, White and etc.)
• Religion (Muslin, Buddhist, Christian).
• Reasons for buying a present (Birthday, Anniversary, and etc.)
• Seasons (Winter, Spring, Summer, Autumn)
• Holidays (Thanksgiving, Halloween)
• Types of pet (Dog, Cat, Hamster)
• Blood groups (Group A, Group B, Group AB, Group O).

When categorical data has only 2 possible values, it is called binary. If we use the categorical data examples above, the results of gender survey (male and female) and the survey on a topic “Do you have children?” (Yes or No) are examples of binary data.

Categorical and Quantitative (Numerical) Data: Difference

Sometimes, it is difficult to distinguish between categorical and quantitative data.

Quantitative data is measured and expressed numerically. It has numerical meaning and is used in calculations and arithmetic.

That is why the other name of quantitative data is numerical.

Examples of quantitative data are: weight, temperature, height, GPA, annual income, number of hours spent working and etc. More examples you can see on the ThoughtGo article “Quantitative Data”.

In comparison, the categorical data does not have any numerical or quantitative meaning. It just describes qualitative characteristics of something.

Types of quantitative data are: ordinal, interval, and ratio. Categorical data is always one type – the nominal type.

The distinction between categorical and quantitative variables is crucial for deciding which types of data analysis methods to use. Quantitative data are analyzed using descriptive statistics, time series, linear regression models, and much more. For categorical data, typically only graphical and descriptive methods are used.

Conclusion

As you see a lot of categorical data examples can be given to understand the meaning and purpose of the qualitative data.

When working with data management or statistical sciences, it’s crucial to clearly understand some of the main terms, including quantitative and categorical data and what is their role.

It is important to get the meaning on the terminology right from the beginning, so when it comes time to deal with the real data problems, you will be able to work with them in the right way.