When you’re gathering data, the way you classify it is just as important as the data itself. It could be for market research, customer feedback, or employee assessments, data classification matters. Either way, it’s easy to overlook, but mislabeling your data types can lead to an inaccurate analysis and misleading results.
That’s why understanding the distinction between nominal vs. ordinal data is so critical!
In this blog post, we’ll walk through what each term means, how they differ, and why it matters. We’ll hone in on why it is especially important when designing assessments or working with tools like Agolix 360 assessments. We’ll also offer real-world examples, tips for proper data collection, and visualizations that work best with each type.
Nominal vs Ordinal Data
Before we unpack each type, let’s start with the broader concept of levels of measurement. In statistics, data can be classified into four main levels:
- Nominal
- Ordinal
- Interval
- Ratio
Each level builds on the previous one in terms of complexity and mathematical meaning. The first two, nominal and ordinal, are categorical data types. This means that they deal with groupings or categories, not numerical values in the traditional sense.
Why does classification matter?
Choosing the wrong type of analysis or chart for your data type can skew your interpretation. You wouldn’t calculate the average of colors, right? Similarly, you wouldn’t treat satisfaction levels like they’re equally spaced numerical data.
What is Nominal Data?
Nominal data refers to categories, but without any sort of natural order or ranking between them.
Imagine you’re organizing a group of people by their hair color, like brown, blonde, or black. There’s no logical reason to place one color “above” the other, right? That’s nominal data in action. It’s purely categorical, meaning the categories are used to identify or classify items rather than to compare them in a meaningful way.
Sure, you can count how many people fall into each category (like “100 respondents from the marketing department”), but you can’t say one group is greater than another in any measurable way. That’s the key: nominal data is about “what” something is, not “how much” or “how good.”
4 Key Characteristics of Nominal Data
- No inherent order
- Purely categorical
- Can be counted, but not ranked or averaged
- Often used to classify or segment data
Common Nominal Data Examples
- Gender (male, female, non-binary)
- Eye color (blue, green, brown)
- Country or region
- Industry type
- Job role or department
Use Cases in Business & Research
Let’s say a company rolls out a 360° Assessment using Agolix to gather feedback on leadership effectiveness. As part of the setup, participants are asked to indicate their role in the assessment — whether they’re a manager, peer, or direct report — along with their department and location. These details are all examples of nominal data, aka categories without any ranking. The goal isn’t to compare which department is “better,” but rather to segment the feedback meaningfully.
When results come in, the assessment admin can filter insights by role or team to spot patterns. This could include, for instance, discovering that peers rate a leader more favorably than direct reports. While the feedback scores themselves involve ordinal data, it’s the nominal categories that bring context and clarity so that leaders can better understand where the feedback is coming from and how to act on it.
What is Ordinal Data?
Ordinal data involves categories with a meaningful order, but the intervals between the categories aren’t consistent or known. Think of rankings, levels, or scales where you know which comes first, but not necessarily how much better or worse one category is from another.
An example of this is a classic five-point customer satisfaction survey: “Very dissatisfied,” “Dissatisfied,” “Neutral,” “Satisfied,” and “Very satisfied.” These responses are clearly ordered, right? You know that “Very satisfied” is better than “Satisfied,” which in turn is better than “Neutral.” That’s ordinal data.
Here’s the catch: even though the categories are ranked, the space between them isn’t consistent or measurable. The emotional gap between “Neutral” and “Satisfied” might not be the same as the one between “Satisfied” and “Very Satisfied.” That’s why, although ordinal data can be ranked, it’s not appropriate to calculate precise averages or treat the differences between values as equal.
Ordinal data is especially useful in evaluative or opinion-based settings. These include rating a product, ranking job applicants, or assessing the severity of an issue. You’ll also find it in educational qualifications or performance levels (needs improvement, meets expectations, exceeds expectations).
In research and business, ordinal data plays a crucial role in interpreting attitudes, preferences, and performance. For example, when measuring how satisfied customers are, or how employees perceive their leadership, ordinal data helps capture those nuances in a structured yet flexible way.
So, while nominal data tells you what, ordinal data tells you where something stands in relation to others — but not by how much. Understanding this distinction can significantly improve how you collect, analyze, and act on your data.
4 Key Characteristics of Ordinal Data
- Ordered categories
- No precise spacing between values
- Can be ranked, but not meaningfully added or averaged
- Great for opinion-based or evaluative data
Common Ordinal Data Examples
- Survey scales (Very satisfied, Satisfied, Neutral, Dissatisfied)
- Education levels (High school, College, Graduate degree)
- Customer service ratings (Excellent, Good, Fair, Poor)
Use Cases in Business & Research
Imagine a company using Agolix to conduct a 360-degree performance assessment for team leaders. As part of the process, raters are asked to evaluate each leader across a series of competencies, such as communication, decision-making, and collaboration. They do so using a five-point ordinal scale, ranging from “Needs Improvement” to “Exceeds Expectations.”
While these categories clearly follow a logical order, the distance between them isn’t fixed. One person’s jump from “Meets Expectations” to “Exceeds Expectations” may not reflect the same level of improvement as someone else’s. Still, the ranking itself is invaluable, offering a structured way to interpret performance levels across different raters. By analyzing these ordered responses, the company can identify not just where leaders are succeeding but where they may need development, without over-interpreting the gaps between ratings.
3 Key Differences Between Nominal and Ordinal Data
While nominal and ordinal data may seem similar at first glance — they both involve categories, after all — the differences between them are actually pretty significant. This is especially true when it comes to how you use and analyze them.
1. Category Structure
Let’s start with how the categories are structured. Nominal data has no inherent order; it’s like sorting socks by color. One isn’t more “advanced” than the other; they’re simply different labels used for identification.
Ordinal data, on the other hand, is ordered by nature. Think of it like organizing a group of students by class standing: freshman, sophomore, junior, senior. There’s a clear progression, even if we don’t know the exact difference in experience or performance between each level.
This distinction affects how we can work with the data. With nominal data, the only valid mathematical operation is counting. There’s no point in ranking or averaging values that don’t follow any order. With ordinal data you can rank responses, giving you insight into direction or preference.
For instance, you can see whether more customers selected “satisfied” over “neutral,” which gives you a better feel for sentiment.
2. Analysis
When it comes to analysis, nominal data is best handled using methods like frequency counts, modes, or chi-square tests, which look at distribution patterns across categories. Meanwhile, ordinal data supports more advanced approaches such as medians, rank-order correlations, and non-parametric statistical tests.
These tools respect the order of the data, without assuming equal spacing between responses.
3. Visualizing Data
If you’re thinking about how to visualize these data types, there are best practices to follow there too. Bar charts and pie charts work great for nominal data, giving you a clear picture of how different categories stack up.
For ordinal data, it’s often more helpful to use ordered bar charts or stacked bars, which preserve the natural sequence and make it easier to interpret trends or progression. Here is a good reference for how to choose charts based on data types.
Here’s a quick cheat sheet to remember the core distinctions:
Why Each Data Type Matters
It’s surprisingly easy to misclassify ordinal and nominal data, especially when designing assessments or surveys. That said, a small mistake can unravel your analysis, leading to conclusions that seem solid on the surface but fall apart under scrutiny.
Let’s take a closer look at why using each data type correctly is so important, and what happens when you don’t.
When You Misuse Ordinal Data
Ordinal data has an order, but the gaps between values aren’t consistent. So treating it like interval data — where values are evenly spaced — can skew your results.
Here’s what can go wrong:
- Misleading averages: Calculating the mean of responses like “Strongly Disagree” to “Strongly Agree” assumes equal spacing, which often doesn’t exist. You might end up with a false middle ground that doesn’t represent how people actually feel.
- Flawed sentiment analysis: You may overlook polarization in feedback, thinking many people are either very satisfied or very dissatisfied, because the average masks the extremes.
- Inaccurate performance comparisons: Using averages in team feedback can create a false sense of who’s doing well or poorly, leading to misguided development plans.
When You Misuse Nominal Data
Nominal data is purely categorical, meaning it has no inherent order. Because of that, applying tools or visuals that imply sequence or progression can send the wrong message.
Common pitfalls include:
- Using line graphs: These suggest a trend or flow that doesn’t exist for categories like department names or job roles.
- Inferring rank from labels: Readers might assume one group is “higher” or “lower” than another, when it’s simply a category without hierarchy.
- Misleading segmentation: Visualizations can imply that categories are directly comparable in value, when they’re not.
Ultimately, these missteps don’t just create technical errors; they have real-world consequences. Imagine using a 360-degree feedback tool to evaluate leaders, and you average satisfaction ratings across roles, assuming they behave like numerical data. You might conclude that a manager is “meeting expectations,” when in reality, half the team thinks they’re excelling and the other half thinks they’re failing. That kind of misread doesn’t just affect your report, but impacts leadership development, team morale, and critical business decisions.
The takeaway? Understanding how to properly classify and analyze your data isn’t optional, but foundational. It protects the accuracy of your insights and the integrity of the choices you make based on them.
Nominal and Ordinal Data Examples in Practice
So, what does all of this look like in action? Let’s walk through a few everyday scenarios where nominal and ordinal data pop up, and how getting them right makes all the difference.
Coaching & HR (Nominal Data)
Let’s say you’re an HR manager rolling out a leadership development program. When collecting feedback, you might record the department or job title of each participant.
These are classic examples of nominal data; they help you group responses but don’t carry any sense of hierarchy. Meanwhile, the actual feedback of how participants rated their leadership skills or growth, is collected using an ordinal scale, such as “beginner,” “developing,” or “advanced.”
This allows you to track not just what teams are involved, but how they’re progressing.
Survey Design (Ordinal Data)
Imagine you’re crafting a customer feedback form. One of your questions asks: “What country do you live in?” which is a nominal question.
The answer could be the U.S., Canada, or Germany, and none of them are ranked or ordered. However, when you ask, “How satisfied were you with your experience?” and offer responses from “Very dissatisfied” to “Very satisfied,” you’ve entered the world of ordinal data.
These responses have a meaningful order, giving you insight into customer sentiment.
Consulting Projects (Nominal & Ordinal Data)
Consultants often juggle both data types in client work. Suppose you’re helping a company assess digital maturity across business units.
First, you segment those units by industry type (retail, finance, healthcare), making this your nominal data. Then, you evaluate each unit’s maturity level using an ordinal scale like “emerging,” “developing,” or “advanced.”
This classification lets you compare progress in a way that’s structured, but still respects the non-numeric nature of the categories.
Final Pro Tip: Always design your questions to match your data needs. Use dropdowns for nominal categories and scales/sliders for ordinal questions. This small attention to detail can make a big impact when it’s time to crunch the numbers or build visual reports.
Collecting and Using Each Data Type
Collecting nominal and ordinal data isn’t just about asking the right questions, but about asking them in the right way.
Let’s start with nominal data. Since these are labels or categories with no inherent order, your goal is to make them as clear and unambiguous as possible. Each option should be mutually exclusive, leaving no room for overlap or confusion.
For example, if you’re asking someone to select their department, list distinct options like “Marketing,” “Sales,” and “HR.” If you include an “Other” option, make sure there’s a space for them to type in a custom answer. This prevents vague categories from muddying your results and ensures that every response can be neatly categorized.
When designing your assessment, single-select options or dropdown menus work well because they allow for clear selection of nominal data.
Collecting and Using Ordinal Data
Ordinal data, on the other hand, requires a bit more thought when it comes to sequencing. Because these responses have an inherent order, it’s essential to arrange them logically.
A typical example is a satisfaction scale that moves from “Very dissatisfied” to “Very satisfied.” Keep your scales balanced — offering equal numbers of positive and negative options — and avoid cramming in too many steps, which can overwhelm respondents and skew the data.
When it comes to survey design, how you format your questions matters just as much as what you ask. Meanwhile, for ordinal questions, it’s best to use Likert scales (like 1 to 5) or descriptive ranking systems.
These not only make it easier for people to answer honestly, but also simplify the process when it’s time to analyze the responses.
Organizing Nominal and Ordinal Data
Once you’ve collected the data, the next step is to store and organize it properly. Even though nominal and ordinal responses are often saved as text, it’s smart to code them consistently behind the scenes.
You can do this by assigning numeric values that make analysis easier. For example, you might code “Very dissatisfied” as 1 and “Very satisfied” as 5, knowing that the numbers represent order, not magnitude.
From there, tools like filters, pivot tables, and dashboards can help you navigate the data for deeper insights. Want to see how different departments rated a new initiative? Cleanly collected and properly categorized data makes it easy to find the answers you’re looking for without second-guessing your results.
In the end, thoughtful question design paired with organized reporting practices is the foundation for reliable, actionable insights. Whether you’re gathering data for internal reviews or customer feedback, getting it right at the collection stage is key.
Choosing the Right Analysis and Visualization
Once your data is collected and organized, the next big step is deciding how to analyze and visualize it. That said, the tools and charts you use need to match the type of data you’re working with. Applying the wrong analysis can lead to misinterpretations, and even well-designed visuals can be misleading if the underlying data isn’t handled correctly.
Nominal Data Analysis and Visualization
Since this type of data represents labels or categories without any order, your analysis should focus on counting how often each category appears. Tools like frequency counts and chi-square tests are perfect for exploring patterns and associations within this kind of data.
Bar charts and pie charts are your best friends here. They allow you to display how many respondents fall into each category in a way that’s intuitive and visually digestible. If you’re looking to identify the most common response, stick with the mode — the value that occurs most frequently — as your go-to measure.
- Statistical Tools: Frequency counts, Chi-square tests
- Visuals: Pie charts, bar charts
- Measure of central tendency: Mode
Ordinal Data Analysis and Visualization
Ordinal data requires a slightly different approach. Because there’s an inherent order, you can do more than just count. This is where medians, percentiles, and non-parametric tests come into play. These tools help you understand not just what people chose, but how their responses rank relative to each other.
For visualizing ordinal data, go with ordered bar charts or stacked column charts. These formats preserve the meaningful sequence of responses, making it easier to spot trends and draw conclusions about distribution or sentiment.
Throughout this process, avoid calculating means or averages unless you’ve confirmed that the intervals between your ordinal values are evenly spaced. For example, assuming that “satisfied” is exactly halfway between “neutral” and “very satisfied” can distort your analysis unless that spacing has been validated. Similarly, stay away from line graphs with ordinal data, as lines imply a continuous and equal progression that ordinal scales don’t offer.
Using Nominal and Ordinal Data in Agolix 360 Assessments
Agolix 360 assessments are designed to capture both nominal and ordinal data for holistic feedback. Great feedback goes beyond simple ratings. It’s about context, clarity, and actionable insights. Agolix 360 assessments are intentionally designed to capture both nominal and ordinal data, offering a multidimensional view of employee performance and workplace dynamics.
Let’s break down how that works in practice.
When participants complete an Agolix assessment, they’re often asked to provide information like their role, their department, or even the language or region of their assessment. These are all examples of nominal data, in that they are straightforward labels that help us group and filter results. They don’t carry any ranking, but they do offer context that helps paint a fuller picture of the feedback environment.
When you layer in ordinal data, and things get even more insightful. Within the assessment, participants might rate a colleague’s performance on a scale that ranges from “Needs Improvement” to “Exceeds Expectations.”
Participants could also indicate their level of agreement with a statement like, “This person demonstrates strong leadership” on a Likert scale of 1-5. These kinds of responses reflect ordinal data by telling us not just what someone thinks, but how strongly they feel about it and in what direction.
By combining these two types of data, Agolix assessments deliver richer, more meaningful analytics. You can also visualize it in smart, intuitive ways.
The best part? Agolix interactive dashboards allow you to drill down into specific segments — filtering by team, region, or rating category — so you’re never left guessing what the data means or how to act on it.
Use Nominal and Ordinal Data to Drive Results
Understanding the difference between nominal and ordinal data might seem like splitting hairs, but it’s not. Analyzing employee feedback, building surveys, or advising clients helps classify your data correctly and leads to smarter decisions and more accurate insights.
By using tools like Agolix 360 assessments, leveraging both types of data allows you to capture feedback more effectively and visualize trends in a meaningful way.
Want to see Agolix assessments in action? Check out our 360 Assessment Tool or head over to the Agolix Insider for real-world tips on building assessments that drive results!
