Nominal Variable
Nominal variable is a type of variable that is used to label or categorize data. Nominal variables are usually used to identify items or objects, such as people, places, things, etc.
Nominal Variable in Research
Nominal variables are often used in research to create categories for analysis. For example, a researcher might use nominal data to group respondents by race/ethnicity, gender, or political affiliation. This type of data is often used in social science research to measure constructs such as attitudes and beliefs.
Nominal data is sometimes also referred to as categorical data. It is important to note that nominal data cannot be quantified. This means that you cannot use mathematical operations (such as addition or subtraction) with nominal data.
Example of Nominal Variable
An Example of Nominal Variable Would be: Gender is a nominal variable with two categories (male and female) and hair color is a nominal variable with multiple categories (blond, brunette, red, etc.). A good way to remember nominal variables is that they are named variables.
Another example of a nominal variable is eye color. You could categorize someone as having brown eyes, blue eyes, or green eyes, but again, there’s no particular order to the categories.
Types of Nominal Variable
There are two main types of nominal variables:
- Unordered
- Ordered
Unordered
Unordered nominal variables do not have a specific order or hierarchy. Examples of unordered nominal variables include gender (male/female), eye color (brown/blue/green), or hair color (blonde/brunette/redhead).
Ordered
Ordered nominal variables have a specific order or hierarchy. Examples of ordered nominal variables include size (small medium large), or weight (light heavy medium). In addition, a nominal variable can be used to represent qualitative information that can be divided into groups.
When to use Nominal Variable
Nominal variables are often used in research studies. They are particularly useful when researchers want to compare groups of people or things. For example, nominal variables can be used to compare men and women, different age groups, or different ethnicities.
There are some important things to keep in mind when using nominal variables.
- It is important to make sure that the categories you create are mutually exclusive and exhaustive.
- You should avoid creating too many categories, as this can make your data difficult to interpret.
- You should always label your categories in a clear and consistent manner.
Purpose of Nominal Variable
The purpose of using a nominal variable is to allow researchers to group together entities that share similar characteristics.
For example, researchers may use a nominal variable to identify different types of animals. In this case, the different types of animals would be the different levels of the nominal variable. Researchers could also use a nominal variable to identify different groups of people, such as men and women. In this case, the two groups would be the different levels of the nominal variable.
Advantages of Nominal Variable
There are some advantages of using nominal variables:
- They are easy to use and understand. Nominal variables are often used to label items in surveys or experiments. This makes them easy to code and analyze.
- They are versatile. Nominal variables can be used to create both dichotomous and polytomous variables. Dichotomous variables have two categories (e.g., male/female), while polytomous variables have more than two (e.g., age group).
- They can be helpful in dealing with missing data. If there is a category for “unknown” or “missing,” this can simplify the analysis by reducing the amount of missing data.
Limitations of Nominal Variable
Some Limitations of Nominal Variable are:
- They are often not as reliable as other types of variables. This is because nominal variables are often based on subjective measures, such as people’s opinions or self-reported behaviors.
- They can be difficult to compare. This is because nominal variables often represent different concepts or ideas, which can make it hard to draw meaningful comparisons between them.
- They can also be Limited in their predictive power. This is because they often only capture a small amount of information about a person or situation, making it difficult to use them to predict future behavior or outcomes.
