When you’re working with data, it’s crucial to understand the different ways that data can be classified and measured. These classifications are called “levels of measurement,” and they determine the types of statistical analyses you can perform.
This article will focus on two of the most fundamental levels: nominal and ordinal data. We’ll break down the difference between “ordinal vs nominal” so you know when to use each one.
For context, there are two additional levels of measurement beyond nominal and ordinal: interval and ratio. But for now, let’s concentrate on understanding nominal and ordinal scales and how they work.
Levels of Measurement: An Overview
In statistics, how you categorize data is important. These categories are called “levels of measurement” or sometimes “scales of measurement.” The level of measurement tells you what kind of math you can do with the data.
There are four main levels:
- Nominal
- Ordinal
- Interval
- Ratio
These levels fall into two broader categories: categorical and numerical.
- Nominal and ordinal data are categorical.
- Interval and ratio data are numerical.
Categorical data is generally less precise than numerical data. Let’s take a closer look at nominal and ordinal data.
Nominal Data: Definition and Characteristics
Nominal data is a type of data that names or labels things; it’s used to put variables into categories. The word “nominal” essentially means “name,” and that’s a good way to think about this type of data.
Characteristics of Nominal Data
Nominal data has a few key characteristics:
- Categories are mutually exclusive. This means that a particular data point can only belong to one category. You can’t be in two categories at the same time.
- Categories are exhaustive. This means that all possible values are represented in the dataset. There’s a category for everything.
- Limited mathematical operations. With nominal data, you can’t really do a lot of math. The most common thing you can do is count how often each category appears.
Examples of Nominal Data
Here are some examples of nominal data:
- Gender (male, female, other)
- Eye color (blue, brown, green)
- Types of fruits (apple, banana, orange)
- Movie genres (comedy, drama, action)
- Blood type (A, B, AB, O)
See how these are all just categories or names? That’s nominal data in a nutshell.
Ordinal Data: Definition and Characteristics
Ordinal data is data that places things into categories with a meaningful order or rank. With ordinal data, you know the order of things, but you can’t assume that the intervals between them are equal.
For example, ordinal data tells you whether something is higher or lower than something else, but it doesn’t tell you how much higher or lower.
Here are some characteristics of ordinal data:
- Categories have a logical order.
- The difference between categories isn’t uniform.
- You can rank and compare categories.
Here are some examples of ordinal data:
- School grades (A, B, C, D, F)
- Customer satisfaction (very satisfied, satisfied, neutral, dissatisfied, very dissatisfied)
- Education level (high school, bachelor’s, master’s, doctorate)
- Income level (low, middle, high)
- Seniority levels (junior, mid-level, senior)
Nominal vs. Ordinal Data: What’s the Difference?
The main difference between nominal and ordinal data is that ordinal data has a meaningful order, while nominal data doesn’t. Think of it this way: nominal data is about naming things, while ordinal data is about ranking them.
This difference affects how you can analyze the data. With nominal data, you can really only count how often something appears (frequency counts) or calculate percentages. For example, what percentage of people in a survey chose “blue” as their favorite color?
With ordinal data, you can do more. You can rank the data, find the median, and determine the mode. For example, you can rank customers based on their satisfaction level (very satisfied, satisfied, neutral, dissatisfied, very dissatisfied).
It’s important not to treat ordinal data like it’s interval or ratio data. For example, if you average customer satisfaction scores (treating “very satisfied” as a ‘5’ and “very dissatisfied” as a ‘1’), you might get a misleading result. The difference between “satisfied” and “very satisfied” might not be the same as the difference between “dissatisfied” and “neutral.”
Frequently Asked Questions
What is an example of ordinal data?
Think about customer satisfaction surveys. You might rate your satisfaction as “Very Unsatisfied,” “Unsatisfied,” “Neutral,” “Satisfied,” or “Very Satisfied.” These categories have a clear order, even though the distance between each one isn’t precisely defined.
What is the difference between categorical and nominal data?
Categorical data is the umbrella term. It includes any data that can be sorted into categories. Nominal data is a type of categorical data, where the categories have no inherent order or ranking. Think of nominal data as labels, while categorical is the broader group these labels belong to.
What is the difference between ordinal and nominal data?
This is the key! The difference is all about order. Ordinal data has a meaningful order or ranking between the categories (like those satisfaction levels). Nominal data doesn’t; the categories are just names or labels without any inherent hierarchy (like colors or types of pets).
What is an example of a nominal and ordinal scale?
A nominal scale example: eye color (blue, brown, green, hazel). There’s no order to these. An ordinal scale example: education level (high school, some college, bachelor’s degree, master’s degree, doctorate). There’s a clear progression or ranking of achievement.
Final Thoughts
Nominal data are used to name or label things, while ordinal data put things in a particular order. Knowing the difference matters, especially if you’re working with data and need to perform statistical analysis.
The kind of analysis you can do depends on whether your data are nominal or ordinal. If you pick the wrong method, you could end up with meaningless results. When you design a survey, spend some time thinking about the data you’re trying to collect, and make sure you’ll be able to analyze it in a useful way.