In statistics and data grouping, you are going to encounter nominal often. Let’s explore its meaning:
Overview: What is nominal?
Nominal is a type of data labeled as mutually exclusive categories that cannot be organized in a set way. The word nominal comes from “in name” in Latin and references data that can merely be labeled. With nominal data, you cannot assign a rank order.
3 Benefits of Nominal
There can be many advantages to choosing nominal as the sort of data collected:
1. Ease of collectibility
Nominal data can be gathered quickly, particularly if there is a digitized system for categorizing the collected data.
2. Fairly well-defined
By asking closed-ended questions in your survey, you are able to get generally well-defined data that can be incorporated quickly.
The collection methods for nominal data are generally reliable.
Why is nominal important to understand?
There are multiple reasons to understand nominal:
1. It is a great entry point to higher levels of measurement.
Since nominal data is the simplest level of measurement, it is a good idea to have a fundamental understanding of it before moving on to the progressively more complex three other levels.
2. Your job may work with data.
If you have a business, your job will likely involve working with data. Therefore, it will be important to understand nominal data as well as how it is collected and interpreted.
3. You can get to know your customer base better.
Nominal data can be utilized to understand your customers more fully and how to appeal to them.
An Industry Example of Nominal
A leisure-wear company is planning out their line for the next season. They want to get a better idea of who is using their products, so they can know what groups they should be trying to appeal to more. The marketing department has a questionnaire drafted to be included with all online purchases in order to collect nominal data. They develop a set of closed-ended questions to give them a better idea of who their end-users are.
3 Best Practices When Thinking about Nominal
Here are a few things to keep in mind when thinking about nominal:
1. Remember that it cannot be ranked.
Nominal data can only be categorized, not ranked.
2. You cannot do arithmetic operations with them.
Since nominal data cannot be ordered in any meaningful way, even if you assign some arbitrary numerical value to the data, you cannot use it to conduct arithmetic operations.
3. Be mindful with your data collection
Some nominal data can be of a sensitive nature. Some examples would be gender, race, or political affiliation. Be mindful of this in the way the data collection is being conducted.
Frequently Asked Questions (FAQ) about Nominal
1. How do you interpret nominal data?
Since nominal data is unable to be quantified, it is primarily grouped into categories. Typically, this is done in alphabetical order.
2. What are the categories of nominal data?
There are no set categories for nominal data, so they can be created depending on the results you intend to measure.
3. What are some examples of nominal data?
Nominal data can be things like hair color, eye color, race, sex, zip code, blood type, and political party.
4. Are there any disadvantages to nominal?
Nominal data can be limiting in that it cannot be quantified and it is the simplest level of measurement. In some studies, not being able to order it can be a disadvantage.
5. How is nominal different from ordinal?
Nominal is the lowest level of measurement, with ordinal being one higher. In nominal data, you are simply listing the variables and they do not need to be in any particular order. With ordinal data, all the variables are in order and named. Examples of ordinal data would be satisfaction rating, income level, and height.
Understanding nominal is an easy way to get a better understanding of the simplest level of measurement. The collection of nominal data can be analyzed to get a better idea of just who is purchasing your goods and how to market to them. Knowing your customers is a fundamental part of business and nominal data can give you some insight.