When working with data sciences, we need to understand what is the difference between ordinal and nominal data, as this information helps us choose how to use the data in the right way.
A data scientist decides how to determine what types of data analysis to apply based on whether the data set is nominal or ordinal.
On this page you will learn:
- What is nominal data and what is ordinal data?
Definition and examples
- Nominal VS Ordinal Data: key differences
- A comparison chart: infographic in PDF.
Nominal and ordinal are two different levels of data measurement. Understanding the level of measurement of your variables is a vital ability when you work in the field of data.
To put it in other words, ways of labeling data are known as “scales”. Actually, there are four measurement scales: nominal, ordinal, interval and ratio. These simply represent methods to categorize different types of variables.
Nominal Data: Definition, Examples, Key Characteristics
First, let’s clarify that nominal data scales are used simply for labeling variables, without any type of quantitative value. The name ‘Nominal’ comes from the Latin word “nomen” which means ‘name’.
Nominal data are those items which are distinguished by a simple naming system. They are data with no numeric value, such as profession. The nominal data just name a thing without applying it to an order related to other numbered items.
The most popular way of thinking about nominal data and variables is that they are just named.
Nominal data are also called categorical data. In the nominal scale, the subjects are only allocated to different categories. The values grouped into these categories have no meaningful order. There is no hierarchy. For example, gender and occupation are nominal level values.
So let’s sum the key characteristics of nominal data and variables:
- Nominal data cannot be quantified.
- It also cannot be assigned to any type of order.
- The values are only allocated to distinct categories.
- Those categories have no meaningful order.
The Nominal Scale
The nominal scale put non-numerical data into categories. Actually, the nominal scales could just be called “labels.” The nominal scales are mutually exclusive (no overlap) and do not have any numerical matter.
For example: Putting countries into continents. Example: Bulgaria is a country in Europe.
Interesting Note: a nominal scale with only two categories (e.g. female/male) is called “dichotomous.”
Examples of Nominal Data:
- Gender (Women, Men)
- Religion (Muslin, Buddhist, Christian)
- Hair color (Blonde, Brown, Brunette, Red, etc.)
- Housing style (Ranch House, Modernist, Art Deco)
- Marital status (Married, Single, Widowed)
- Ethnicity (Hispanic, Asian)
- Eye color (Blue, Green, Brown).
As you see from the examples above there is no intrinsic ordering to the categories. Eye color is a categorical variable having a few categories (Blue, Green, Brown) and there is no way to order these from highest to lowest.
In fact, a lot of market segmentation examples are a basis for creating nominal scales and measurement.
Ordinal Data: Definition, Examples, Key Characteristics
If we need to define ordinal data, we should tell that ordinal number shows where a number is in order. This is the crucial difference with nominal data.
Ordinal data is data which is placed into some kind of order by their position on the scale. For example, they may indicate superiority. However, you cannot do arithmetic with ordinal numbers because they only show sequence.
Ordinal data and variables are considered as “in between” categorical and quantitative variables. In other words, the ordinal data is a categorical data for which the values are ordered.
In comparison with nominal data, the second one is a categorical data for which the values cannot be placed in an ordered.
The ordinal numbers and values indicate direction, in addition to providing nominal information.
We can also assign numbers to ordinal data to show their relative position. But we can not do math with those numbers. For example: “first, second, third…etc.” With this in mind, we cannot treat ordinal variables like quantitative variables.
We use ordinal variables to describe data that has some kind of sense of order. However, you cannot be sure that the intervals between the sequacious values are equal.
So let’s sum the key characteristics of ordinal data:
- Ordinal data is placed into some kind of order.
- Ordinal numbers only show sequence.
- We can assign numbers to ordinal data.
- We cannot do arithmetic with ordinal numbers.
- We don’t know whether the differences between the values are equal.
As you guess, ordinal scales are build up of ordinal data. In ordinal scales, the order of the value is important. The differences between each value are not really known and not important.
It helps to define if the item has more or less of a trait as compared to another item.
Some of the most popular examples of the ordinal scale are occupational status, the ranking of participants in competitions and tournaments, school class rankings: 1st, 2nd, 3rd and etc.
In market research, ordinal scales are widely used to measure relative perceptions, preferences, and opinions.
Examples of Ordinal Data:
- The first, second and third person in a competition.
- Education level with values of the elementary school education, high school graduate,
- When a company asks a customer to rate the sales experience on a scale of 1-10.
- When customers rank brands on the basis of their preferences.
- Pay bands in a company, as indicated by A, B, C, and D.
- Letter grades: A, B, C, and etc.
- Economic status: low, medium and high.
- When you perform a survey and ask respondents to express their level of satisfaction with the choice of those words: very satisfied, satisfied, neutral, dissatisfied, very dissatisfied.
- When a respondent should put a value from 1 to 3 to a statement. Often the words “agree, neutral, disagree” are used.
As you see from the examples above, the ordinal scale shows the relative position of the items but not the differences between the items.
Comparison Chart: Nominal vs Ordinal Data
Nominal and ordinal data have their important role in statistical and data sciences.
You should know what you can do with ordinal and nominal data. You should know how to measure them. The two scales of measurement (ordinal and nominal) depend on the variable itself.
Knowing the level of measurement of the variables is important in many business situations. Each of the measurement scales provides a different level of detail. Nominal scales provides the least amount of detail. On the other hand, ordinal scales provide a higher amount of detail.
Understanding the difference between nominal and ordinal data has many influences such as: it influences the way in which you can analyze your data or which market analysis methods to perform.
Download the following comparison chart/infographic in PDF for free: Nominal data vs Ordinal data
Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.
Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.