What is ordinal data? A simple explanation with examples

Ordinal data is one of four statistical data types: nominal, ordinal, interval, and ratio. This type of data measurement is often used in marketing, research, economics, and financial services.  

By leveraging ordinal data, you can gain valuable insights into customer behavior and introduce a hierarchic order to the collected information for further analytics.

Let's take a closer look at what ordinal data is and how it applies to your business.

Ordinal data is classified data with an order or a ranking. It's a type of qualitative data that groups information into ordered categories.

Businesses often work with ordinal data when they analyze customer survey responses. An example of this type of data is the level of education. You can group customers by their level of education, from high school diploma to doctorate.

Ordinal data categories always have a pre-set natural order. You can't get a doctorate before getting a bachelor's degree or earn a bachelor's degree before finishing high school.  

One of the most recognizable features of ordinal data is the lack of value in the intervals between data. The differences between data points can't be determined or have no meaning.

For example, the time interval between getting a high school diploma and a bachelor's degree can drastically differ from the interval between bachelor's and master's degrees. Meanwhile, this information doesn't provide any value to a marketing specialist grouping the target audience by its level of education.

Ordinal data can't be analyzed using mathematical operators. That's why you can't find an "average" value, but determining a "median" is possible.

Overall, the key elements of ordinal data are:

• Ordinal data is non-numeric

• There is always a hierarchy or order (that's why the data is called ordinal)

• Ordinal data values don't have an even distribution

• The results of ordinal data analytics are frequency distribution, median, and range of variables.

It's an excellent tool for studying and analyzing information when precision isn't a necessity.

The easiest way to understand ordinal data is by studying common examples, such as: 

Income level

• Low level

• Middle level

• Upper level

Level of education

• Primary

• Secondary

• Post-secondary

Age

• 0–12

• 12–18

• 18–25

• 25–35

• 35+

One of the most common examples of ordinal data is the Likert scale. This points scale is designed to rate a person's opinion about a subject.

An example of a Likert scale looks like this:

How satisfied are you with our customer service?

• Very satisfied

• Satisfied

• Neutral

• Unsatisfied

• Extremely unsatisfied

While ordinal data is more complex than nominal data, it still doesn't provide extensive information about the subject. However, it can provide valuable insight into human behavior.

Ordinal data is one of the four common data types. Let's see how it compares with the rest of them.

Ordinal data vs. nominal data

Nominal data is the simplest form of a scale of measure. You can use this data type to label variables without adding any quantitative value or order. 

Examples of nominal data are:

• Male/female

• Animal/fish

• Blond hair/brown hair

To analyze nominal data, you can group it into categories and determine the frequency. Meanwhile, ordinal data takes nominal data to the next level by giving these valuables an order or a hierarchy. In short, it categorizes and labels data points.

Ordinal data vs. interval data

Interval data takes another step towards providing a more precise measurement. Besides categorizing and ordering data as nominal and ordinal data does, it also implements equal intervals between neighboring data points. 

Examples of interval data include:

• Temperature

• IQ scores

• Income ranges

While interval data has pre-set intervals, intervals between data points in ordinal data can be random. They provide no value for data analysis.

Ordinal data vs. ratio data

Similar to ordinal data, ratio data can be categorized and ranked. There are also equal intervals between data points (as in interval data). In addition, ratio data has a true zero. True zero is an absolute absence of a variable. For example, if you are analyzing income, market share, weight, or height, there is always a zero.

The easiest way to collect ordinal data is by using questionnaires and surveys. Businesses use this type of data collection to gain more information about their customers.  

Being classified into categories is psychologically easier than providing precise answers. Customers are often willing to answer questions that collect ordinal data because they don't feel invasive. For example, a customer may be more willing to say that their income is between $20,000 and $40,000 than to mention an exact number.

Ordinal data is extremely useful in the financial, marketing, and insurance sectors. Common applications include:

Marketing

Marketers use ordinal data for many purposes, including:

• Building a buyer's persona

• Evaluating customer satisfaction

• Monitoring customer behavior

• Gaining insights into market trends

Regularly arranging ordinal data surveys and analyzing them correctly, you can streamline marketing strategies, improve customer satisfaction, increase retention, and more.

Medical research

Ordinal data can be instrumental in medical studies and clinical trials. Researchers may arrange a survey to determine how people feel after taking a certain medication. 

For example, they can ask, "Did your mood improve after taking this drug?"

• Stayed the same

• Slightly improved

• Significantly improved

While it's impossible to measure mood improvements precisely, such responses can provide data for analytics.

Education

Schools and universities use ordinal data to evaluate student experience and make adjustments to improve how students are educated.

An example is a survey with questions like "How comfortable do you feel asking questions in class?"

• Very comfortable

• Comfortable

• Uncomfortable

• Very uncomfortable

Like customer experience surveys, student experience surveys provide valuable insight into how schools, colleges, and universities operate from a user’s point of view.

The best way to analyze ordinal data is to visually represent the variables. For example, bar graphs can help you understand how many people from your target audience belong to the same category.

You can find out that most of your customers are between ages 25 and 35 or learn that more than a thousand have doctorate degrees.

Statistical tests that can help you analyze ordinal data include:

• Mood's median test: This test allows you to compare medians (middle values) from two or several samples of populations, so you can see the difference between them.

• Mann-Whitney U test: This test allows you to compare two independent samples and see whether they belong to the same population.

• Wilcoxon signed-rank test: This test allows you to compare the scores' distribution in two dependent data samples to see if populations' means differ.

• Kruskal-Wallis H test: This test allows you to compare the mean across three or more independent data samples.

These methods seem complicated and hard to grasp at first. With the right tools, it's possible to analyze ordinal data without getting deep into the methodology. Depending on the goal of data analysis, you can determine the need for in-depth data testing. In most cases, a simple bar graph can provide all the information you need.  

However, if you want to use this data to predict trends, you may need to go deeper into inferential statistics and implement the tests mentioned above.

Ordinal data can provide extra insight when evaluating different segments of your target audience. While it's not precise, this data provides valuable insights into customer behavior. You can also use it to predict behavioral trends, possible new customer segments, product development possibilities, and much more.

Continuous analytics can help streamline customer relationships and improve your marketing strategies. Creating the right survey questions and answer variants is critical to uncover the data you want. You can gather this data throughout the customer's lifecycle with the company through regular surveys.

Examples of ordinal data variables are education (high school, bachelor's, doctorate), age ranges (0–18, 18–25, 25–45), and income levels ($10,000–$20,000, $20,000–$30,000, $30,000–$40,000).

Depending on the question, age can be a nominal or ordinal variable. If the question is "How old are you?" it's a nominal variable. If the question is "What age range are you in?" it's an ordinal variable.

No. Gender is an example of a nominal variable. Ordinal variables can be put in an order. For example, income level can be described in ranges and put into a certain order ($10K–$20K, $20K–$30K, $30K–$40K). You can't do the same with gender.

Height is neither nominal nor ordinal. It's a ratio variable. It can be categorized and ordered with equal intervals and a true zero.