Understanding the differences between nominal, ordinal, interval, and ratio data is essential for accurate data analysis and interpretation. Nominal and ordinal data are categorical—nominal labels items without order, while ordinal ranks them but lacks measurable intervals. Interval and ratio data are quantitative, with ratio data having a true zero point. Choosing the right data type influences your statistical methods, visualization techniques, and research outcomes. Nominal data uses frequency counts and Chi-square tests, while ordinal data supports median and percentile analysis. Recognizing how each data type functions ensures valid results, improves survey design, and enhances the accuracy of insights in data analytics and research.
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Are you the type of person who works better with a bit of freedom and the ability to “shoot from the hip”? Or do you like things done in a strict and orderly fashion every time? This is sort of like nominal vs ordinal data. Nominal and ordinal data are both considered categorical data variables, but are used quite differently.
While nominal and ordinal data are the focus here, it’s important to note the two other types of data measurement scales in research and statistics, interval and ratio data, which are numerical, or quantifiable, data. These four types—nominal, ordinal, interval, and ratio—make up the main levels of measurement, a hierarchical framework that classifies data based on measurement precision. Understanding the level of measurement, including the distinctions among nominal, ordinal, interval, and ratio, is essential for proper data analysis. We’ll touch on those briefly as well later.
What is Nominal Data?
Nominal data (from the Latin word “nomen” meaning “named” data) is data that names or labels variables without a numerical value. The simplest example would be “yes” or “no.” Other examples of nominal data include colors (such as red, blue, green), states (California, Texas, New York), or modes of transportation (car, bus, train). These are two categories, but there is no way to order them from highest to lowest or best to worst. The same could be said for a demographic question like “race.” While there will be numerous categories that can be selected, again, there’s no type of order that can be assigned to them.
So, nominal data is used to gather some information about a group or set of events that are limited to just counts. The characteristics of nominal data are that it is qualitative, lacks any inherent order, and is used for labeling or identifying items. The data type represents a fact, not a preference. Nominal data cannot be ordered, measured, or manipulated mathematically, which highlights its limitations. It is purely descriptive, providing only categorical information with no quantitative meaning.
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Sometimes, number labels are used in nominal data (for example, assigning 1 for blue, 2 for green, 3 for red), but these numbers are just identifiers and do not represent any order or value. Each response or item in nominal data is grouped into one category, with no overlap or hierarchy between categories. Nominal data does not have a quantitative or numeric value, even if numbers are used as labels.
What is Ordinal Data?
Ordinal data is data that is “orderly.” The variables get a number, indicating their ranking on a list. Ordinal scale data is used to rank items or categories in a specific order, but the differences between ranks are not precisely measurable. Researchers use this data to assign labels to opinions. In the hierarchy of data measurement, how ordinal data fits is between nominal data (which has no order) and interval data (which has measurable differences), as described by Stanley Stevens.
For example, when a restaurant asks customers how pleased they were with service, the customer may be able to select 1–5, with 1 being poor and 5 being excellent. Other examples of ordinal data include education level (such as high school, bachelor’s, or master’s degrees) and economic status (such as low, middle, or high), where the categories have a natural order. So, the numbers have an order or rank, as 5 is clearly better than 1. It’s important to note that although ordinal data assigns a numerical value to an opinion, it’s not a quantitative measure because although a ranking of 5 is better than 1, it doesn’t mean it’s necessarily five times better.
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Characteristics of Data
Understanding the characteristics of data is fundamental in data science and data analytics. Data can be classified into several types based on how it describes or measures variables. Nominal data is a form of qualitative data that simply labels variables—think of it as putting names or tags on different categories, such as hair color or preferred mode of transport. This type of data does not provide any quantitative value and cannot be ordered or measured; it only groups variables into distinct categories.
Ordinal data, on the other hand, is also qualitative but introduces a meaningful or natural order among the categories. For example, satisfaction levels like “poor,” “average,” and “excellent” have a clear ranking, even though the exact difference between them isn’t measured. This intrinsic ordering allows ordinal data to classify variables that can be compared in terms of position or rank, but not in precise amounts.
Interval and ratio data, meanwhile, are quantitative and allow for more detailed analysis. Interval data measures variables along a scale with equal intervals but no true zero, such as temperature in Celsius. Ratio data, like height or income, has both equal spacing and a true zero point, making it possible to compare absolute quantities.
Recognizing these characteristics—whether data only labels variables, classifies them into distinct groups, or provides a natural order or measurable difference—is essential for choosing the right methods to analyze and interpret your data.
Data Classification
Data classification is a crucial step in organizing and analyzing information effectively. It involves grouping data into categories based on their characteristics, which directly impacts the statistical methods and visualizations you can use.
Nominal data is classified into distinct groups without any inherent order. For example, classifying people by their favorite color or employment status creates categories that are mutually exclusive but not ranked. Ordinal data, in contrast, is grouped into categories that have a meaningful order or ranking—such as education levels (high school, bachelor’s, master’s, doctorate) or satisfaction ratings (strongly disagree to strongly agree).
Interval and ratio data are classified based on their quantitative values and the equal spacing between those values. Interval data, like dates or temperature, has equal intervals but no true zero, while ratio data, such as weight or age, features both equal spacing and a true zero point, allowing for a full range of mathematical operations.
Proper data classification ensures that your analysis accurately reflects the nature of your data, helping you select the right statistical tests and create visualizations—like bar charts for nominal data or ordered bar charts for ordinal data—that best represent your findings.
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7 Considerations for Using Ordinal vs Nominal Data
Nominal and ordinal data have an important role in statistics and surveying, so it’s important to understand what you can and can’t do with each of them, as well as how to measure them. Ordinal scales usually provide a high level of detail, while nominal scales limit details. Understanding these differences can influence which type you choose and how you’ll analyze your data.
To analyze nominal data, you can use methods such as frequency distributions, pie charts, and descriptive statistics to summarize and visualize the data. Nominal data analysis involves organizing, visualizing, and summarizing categorical data to better understand patterns and trends. Descriptive statistics are useful for summarizing nominal and ordinal data, such as identifying the mode or creating a frequency distribution table. A frequency distribution or frequency distribution table can help organize and interpret responses for both nominal and ordinal data. Pie charts are a useful tool for visualizing nominal data, making it easier to see the distribution of various categories. For statistical testing, non-parametric tests, such as the Chi-square test, are appropriate for nominal and ordinal data. Inferential statistics can be used to dig deeper into the data, going beyond simple summaries to test hypotheses and draw conclusions. The fit test, specifically the Chi-square goodness of fit test, allows you to compare observed and expected frequencies in nominal data. For example, public transport can be a nominal category analyzed in survey data to understand travel preferences. Data can be grouped into various categories, and these categories are used to classify variables based on their characteristics. The process classifies variables into nominal, ordinal, interval, or ratio types. Nominal variables are categorical variables without order, and relationships between two nominal variables can be analyzed using the Chi-square test of independence.
1. Data Analysis
Data analysis will be different depending on whether you choose a nominal or an ordinal scale. During analysis, nominal data is grouped into categories, usually with a percentage (40% said yes, 60% said no). For ordinal data, more complex computations are performed, often determining the mode, median, and other positional measures like quartiles, percentiles, etc.
2. Types of Questions
The types of questions you ask will likely vary depending on whether you are collecting ordinal or nominal data. Nominal data collection often involves yes/no questions, thumbs up/down, or multiple-choice questions. Nominal-minded questions are also sometimes open-ended (allowing the person to write in a response). For ordinal questions, most researchers will employ a Likert scale, interval scale, rating scale, etc. On these scales, respondents rate their experiences or satisfaction levels, which directly affects how the data is classified and analyzed in statistical interpretation. Even though these collection techniques differ from each other, a single questionnaire could use both nominal and ordinal data collection techniques (and many surveys do).
3. User-Friendliness
Unless you’re offering incentives or have a very engaged audience, nominal data collection is typically more time-consuming for participants and less likely to get high response rates. That’s because they have to consider multiple choices or “fill in the blanks” of an open-ended question, which some may find tiring. On the other hand, ordinal data simply requires them to choose from a scale, simplifying the process. Emojis can even be added to the numbers (1 = 😠, 2 = 😔, 3 = 😑, 4 = 🙂, 5 = 😀) making the survey even more fun or user-friendly.
4. Freedom of Expression
Ordinal data requires people to select from a scale, which is great for data analysis but doesn’t offer respondents freedom of expression. For example, if someone is going to rate service as poor, they may want to write in exactly why it was poor (for example, they may want to explain that the waiter was great, but the food was terrible). Of course, researchers can choose the option to provide an ordinal scale followed by a nominal, open-ended question (tell us more about your experience…). This gives a business an idea of a customer’s satisfaction level and what influenced their feelings.
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5. Inclusiveness
Going hand-in-hand with #4 is the matter of inclusiveness. With ordinal variables, respondents are restricted to a set of pre-defined options. This is also true of many multiple-choice ordinal questions, which often rely on multiple choice. However, ordinal questions can also be open-ended, providing freedom of expression by allowing people to write in their responses. So, for example, when asking about gender, respondents write in their answers so that they’re not locked into a strict male/female answer for non-binary or transgender individuals. This allows them to identify as they choose and keeps the survey more inclusive. Read more about DEI (Diversity, Equity, and Inclusion) Surveys.
6. Eliminating Irrelevant Data
Giving respondents the freedom to express themselves can come at a time cost for researchers, which should also be considered based on how quickly results need to be compiled. Nominal data, while often providing valuable insight, can also include irrelevant data that analysts will need to weed through. So, it’s important to weigh what you hope to achieve with the research or survey when deciding which type of data to collect. Consider a question about the performance of a president. In an ordinal question, you may ask a respondent to rate the president’s performance; now, the respondent may be happy with the job the president is doing, but find a recent personal scandal embarrassing. So they assign a 2 out of 5, which reflects poorly on performance, even though that’s not the issue. A nominal, open-ended question, however, could have differentiated between performance and the scandal.
7. Ease of Comparison
Finally, how easily do you need to compare responses? Nominal data isn’t always easy to compare; a question could have 8 multiple-choice answers, leading to a wide variety of possibilities, or open-ended questions, which can be hard to assign value to. On the other hand, ordinal data is very easy to compare, making it extremely convenient to group the variables after ordering them.
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Examples and Applications
To better understand the differences between nominal and ordinal data, let’s look at some practical examples and their applications in data science and data analytics.
Mathematical Operations and Data Types
The type of data you collect determines which mathematical operations and statistical analyses you can perform. Nominal data, lacking any quantitative value, cannot be manipulated with mathematical operators. For example, you cannot add or average hair colors or employment statuses. The only measure of central tendency that applies to nominal data is the mode, which identifies the most frequently occurring category in your sample data.
Ordinal data, while still qualitative, allows for some statistical analysis because of its inherent order. You can determine the median or percentiles, which show the middle or specific positions within the ordered categories. However, you cannot perform meaningful addition or subtraction, as the intervals between categories are not necessarily equal.
Interval and ratio data, being quantitative, support a full range of mathematical operations. Interval data, such as temperature, allows for addition and subtraction due to equal spacing between values, but lacks a true zero, so ratios are not meaningful. Ratio data, like weight or income, has both equal spacing and a true zero point, enabling all mathematical operations, including multiplication and division.
For example, in statistical analysis, you might use the mean to analyze interval data, the median for ordinal data, and the mode for nominal data. Understanding which mathematical operations are appropriate for each data type ensures accurate and valid results in your data analytics and research.
Ratio and Interval Variables
Just to keep you informed on the other types of data used in research and statistics—interval and ratio data—we wanted to briefly cover them as well. This data is classified as numerical, or quantifiable, data. Ratio variables begin with zero, representing equality between two things (versus ordinal which don’t represent equality between things, as previously mentioned). Ratio data represent relative differences. For example, comparing the population of the United States to China, a ratio variable might take the US as the zero-base with 311 million people, which gives China, with 1.3 billion people, a ratio value of 4.29. That means China has 4.29 times as many people as the United States.
Interval variables, on the other hand, can dip below zero. Income ranges are another example of interval data, similar to temperature or SAT scores, as they are categorized as numerical variables with equal intervals. They hold no true zero and can represent values below zero, for example, in the case of temperature. It’s possible to measure temperature below 0 degrees Celsius, such as -10 degrees, requiring an interval scale. Measuring height or weight? These go from 0 and up, requiring a ratio variable.
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Conclusion
When embarking on a study or survey, it’s important to decide whether you want to collect nominal or ordinal data. Of course, you can also choose a combination of the two. There are many considerations to make when deciding which is best for your research, which we’ve outlined here. When you’re ready, start your nominal or ordinal data survey. SurveyLegend is here! Our surveys are easy to use, and you can choose which type of questions to ask and which type of data you want to receive back. Be sure to download our guide, How To Write Survey Questions Like an Expert, for more information!
Which type of data collection do you prefer, nominal vs ordinal data? Or, do you prefer a combination of the two? Let us know in the comments!
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Frequently Asked Questions (FAQs)
What is nominal data?
Nominal data adds a name or label variables without a numerical value. Answers are typically yes/no, multiple choice, or open-ended.
What is ordinal data?
Ordinal data assigns numbers to answers to indicate a ranking. Use Likert scales when asking someone to rank their level of service following a transaction.
What are the advantages and disadvantages of nominal and ordinal data?
Nominal data gives respondents more opportunity to express themselves, allowing them to choose multiple answers or fill in open-ended questions. However, this can make it more difficult to analyze. Ordinal data is very easy to compare and allows researchers to quickly come to conclusions. However, it doesn’t provide context for the answers. So, many researchers use a combination of the two.
What are nominal vs ordinal data examples?
An example of a nominal variable would be the demographic question of “race.” Respondents can choose between multiple answers. An ordinal data example would be asking someone to rate the level of service they received.
