Nominal Data

Nominal data, also known as categorical data, is a type of data used in statistics to label variables without providing any quantitative value. The key characteristic of nominal data is that it categorizes data into distinct groups that do not have a specific order or ranking. This means that while the data can be divided into categories, these categories cannot be meaningfully arranged in a hierarchical order.

For example, consider the variable "favorite ice cream flavor." The possible categories could be chocolate, vanilla, and strawberry. These categories do not have an intrinsic order – chocolate is not inherently "better" or "worse" than vanilla or strawberry.

In this article, we will discuss this concept of nominal data including examples and collection methods.

What is Nominal Data?

Nominal data is a type of categorical data where the categories are distinct and have no inherent order or ranking. It is used to label or name variables without assigning any quantitative value to them. Each value in nominal data is mutually exclusive, meaning that no individual item can belong to more than one category.

Nominal data is used to label variables without any quantitative value. Examples include gender (male, female), eye color (blue, green, brown), and types of cuisine (Italian, Chinese, Mexican).

Definition of Nominal Data

Nominal data, also known as categorical data, is a type of data used to label variables without providing any quantitative value.

Characteristics of Nomial Data

Characteristics of nominal data are:

• Nominal data represents categories or names. Each value in nominal data is distinct and falls into a specific category.
• The categories in nominal data do not have a specific order or ranking. One category is not considered higher or lower than another.
• Each observation belongs to one and only one category. There is no overlap between categories.
• Arithmetic operations such as addition, subtraction, multiplication, or division cannot be performed on nominal data.
• Nominal data is qualitative, meaning it describes qualities or characteristics rather than quantities.

Examples of Nominal Data

Some examples of nominal data are:

• Gender: Male, Female, Non-binary, Other
• Marital Status: Single, Married, Divorced, Widowed
• Nationality: American, Canadian, Indian, Chinese, Australian, etc.
• Blood Type: A, B, AB, O
• Eye Color: Brown, Blue, Green, Hazel, Black
• Brand of Car: Toyota, Ford, Honda, BMW, Tesla
• Types of Cuisine: Italian, Mexican, Chinese, Indian, Thai
• Political Party: Democratic, Republican, Independent, Green
• Types of Pets: Dog, Cat, Fish, Bird, Hamster
• Favorite Sports: Soccer, Basketball, Tennis, Cricket, Baseball

How to Collect Nominal Data?

Collecting nominal data involves gathering information that can be categorized without a numerical value or order. Here are some methods to collect nominal data:

Surveys and Questionnaires: Design surveys with multiple-choice questions where respondents select their answers from predefined categories.

• Example: "What is your favorite type of cuisine?" with options like Italian, Mexican, Chinese, Indian, etc.

Interviews: Conduct structured or semi-structured interviews with questions designed to elicit categorical responses.

• Example: Asking interviewees about their marital status or political affiliation.

Observation: Observe and record data based on visible categories.

• Example: Noting the types of cars in a parking lot (Toyota, Ford, Honda, etc.).

Administrative Records: Utilize existing records or databases that contain categorical information.

• Example: Hospital records with patients' blood types or educational institutions' records of student majors.

Online Forms: Create online forms with dropdown menus or radio buttons for categorical questions.

• Example: An online registration form asking for gender with options Male, Female, Non-binary, Other.

Visualization Techniques for Nominal Data

Some common techniques for visualization for nomial data are:

• Frequency Tables
• Bar Charts
• Pie Charts

Frequency Tables

Frequency tables list the categories and their corresponding counts or frequencies. While not a graphical method, they provide a clear and precise summary of the data.

For instance, if in some quesnire which asks 100 people about their favourite fuirt out of "Apple, Banana, Orenge, Starberry, Grapes" i.e.,

Strawberry, Grapes, Grapes, Grapes, Grapes, Apple, Grapes, Strawberry, Grapes, Orange, Grapes, Orange, Apple, Banana, Banana, Banana, Apple, Apple, Banana, Apple, Orange, Grapes, Banana, Banana, Grapes, Banana, Apple, Grapes, Banana, Grapes, Banana, Strawberry, Banana, Strawberry, Grapes, Banana, Apple, Orange, Grapes, Apple, Orange, Banana, Strawberry, Banana, Orange, Apple, Banana, Apple, Orange, Orange, Banana, Strawberry, Banana, Banana, Strawberry, Strawberry, Banana, Apple, Apple, Banana, Grapes, Strawberry, Grapes, Apple, Grapes, Orange, Apple, Banana, Apple, Apple, Banana, Apple, Banana, Apple, Banana, Apple, Banana, Orange, Banana, Orange, Banana, Banana, Apple, Strawberry, Orange, Apple, Grapes, Banana, Banana, Apple, Grapes, Orange, Apple, Banana, Apple, Grapes, Apple, Grapes, Orange, Orange

Frequency table for this data is:

Bar Charts

Bar charts are one of the most straightforward ways to visualize nominal data. Each category is represented by a bar, and the length or height of the bar corresponds to the frequency or count of that category. This method allows for easy comparison between categories.

Bar Chart for the above mentioned data is as follows:

Pie Charts

Pie charts display data in a circular format, where each slice represents a category's proportion relative to the whole dataset. This visualization is useful for showing the parts of a whole.

For the data discussed above we can represent the same data as

Pie chart for this data is:

Nominal vs. Ordinal Data

Read More about Nominal vs Ordinal Data.

Conclusion

In summary, nominal data helps to classify and label variables, enabling easy grouping and comparison without implying any numerical value or order. This makes it a fundamental type of data for many fields, including marketing, healthcare, and social sciences.

Read More,

• Statistics
• Types of Data in Statistics
• Ordinal Data
• Qualitative Data
• Quantitative and Qualitative Data