25 Nominal Data Examples

nominal data definition and examples, explained below

Nominal data refers to non-numerical data that is not inherently ordered. It is a sub-type of categorical data, and can include categories like gender, race, and color (Babbie, Halley, & Zaino, 2007).

If you’re writing an essay on nominal data, it might be worthwhile quoting a scholarly definition. Here are some good ones I found:

  • “Nominal data is a type of categorical data that consists of categories or labels that do not have a natural order or ranking.” (Thiagarajan, 2023)
  • “Nominal data is a type of qualitative data that has no intrinsic order among the categories. For example, gender (male, female), colors (red, green, blue), or types of cuisine (Italian, Chinese, Mexican) are nominal data.” (Hermans, 2023)

Below are some prominent examples.

Nominal Data Examples

1. Gender
Gender is a nominal variable with categories to represent identity. Traditionally, we would divide this into binary data – male and female, but today we often see other categories such as “nonbinary” and “other.”

2. Marital Status
Categories for marital status may be diverse, such as “never married”, “married”, “divorced”, “widowed”, and “in a civil partnership”. There’s no implicit ranking or order among these statuses.

3. Nationality
Nationality is categorized as nominal data because there is no clear hierarchy or order. For instance, labels like “French”, “Indian”, “South African”, “Japanese”, and “Mexican” are identifiers that denote a person’s country of origin or citizenship.

4. Eye Color
Eye color categories could include “blue”, “green”, “brown”, “hazel”, and “grey”. These colors don’t suggest any intrinsic hierarchy or ranking.

5. Hair Type
Hair type can be categorized nominally. Examples of these categories are “straight”, “wavy”, “curly”, and “coiled”. There’s no inherent order or superiority among these types.

6. Blood Type
The primary categories for blood type are “A”, “B”, “AB”, and “O”. None of these blood types inherently ranks above the other.

7. Religion
Religion is another nominal variable. Some categories might be “Christianity”, “Islam”, “Hinduism”, “Buddhism”, and “Judaism”. These religions don’t have an inherent order or ranking among them.

8. Type of Dwelling
Type of dwelling may be a nominal category found in urban planning or census surveys. Categories could be “apartment”, “house”, “condo”, “townhouse”, and “loft”. No particular type is inherently superior or ranked above the others.

9. Occupation
Occupation serves as a nominal variable because we can’t rank or inherently measure occupations on a scale. Categories might encompass “teacher”, “engineer”, “doctor”, “artist”, and “lawyer”. These professions don’t suggest an obvious sequence among them.

10. Car Brands
Car brands cannot be inherently or naturally ranked in the same way as datasets like “height” or “age”. Examples of car brands include “Toyota”, “Ford”, “Mercedes-Benz”, “BMW”, and “Tesla”. No brand inherently ranks above another based solely on its name.

11. Animal Species
Animal species can be categorized nominally. Some examples are “lion”, “eagle”, “shark”, “elephant”, and “penguin”. There’s no inherent ranking or order among these species.

12. Types of Fruits
Types of fruits don’t have a clear hierarchy. Categories might be “apple”, “banana”, “cherry”, “pineapple”, and “grape”. No particular fruit is intrinsically superior to another.

13. Musical Genres
Examples of musical genres could be “rock”, “jazz”, “hip-hop”, “classical”, and “country”. These genres don’t have an inherent order or ranking.

14. Beverage Choices
Beverage choices can include “coffee”, “tea”, “juice”, “soda”, and “water”. No beverage inherently ranks above the other based purely on its type or name.

15. Academic Disciplines
Academic disciplines can include “physics”, “history”, “literature”, “mathematics”, and “biology”. No discipline inherently ranks above another, so we consider them to be nominal.

16. Types of Clothing
Types of clothing can be categorized nominally, while clothing sizes would not because sizes have obvious rankings. Some categories might be “t-shirt”, “sweater”, “jacket”, “dress”, and “shorts”.

17. Game Genres
Game genres could be “action”, “strategy”, “puzzle”, “role-playing”, and “simulation”. No genre inherently ranks above another based on its label.

18. Language Spoken
Language spoken might encompass “English”, “Spanish”, “Mandarin”, “French”, and “Arabic”. We typically see these in census surveys. These languages don’t have an inherent order or superiority.

19. Learning Styles
Learning styles are a form of nominal data that are highly controversial because they are somewhat arbitrary. The common categories include “visual”, “auditory”, “kinesthetic”, “reading/writing”, and “logical”. These styles suggest individual preferences for acquiring knowledge rather than ranks.

20. Types of Flowers
Types of flowers can be categorized nominally. Examples are “rose”, “lily”, “sunflower”, “daisy”, and “orchid”. No particular flower inherently ranks above another based on its type.

21. Pizza Toppings
Pizza toppings on a restaurant menu aren’t ranked and ordered. They’re simply a list of options. Categories might encompass “pepperoni”, “mushrooms”, “olives”, “pineapple”, and “sausage”. These toppings don’t have an inherent order or ranking among them.

22. Book Genres
Some categories of book genres include “mystery”, “fantasy”, “romance”, “science fiction”, and “non-fiction”. These genres don’t suggest any intrinsic hierarchy. For non-fiction books, which would also be nominal, we have created a system called the Dewey decimal system to impose order on categories without any inherent order in them.

23. Types of Footwear
Types of footwear can be considered nominal, while sizes would not. Examples of footwear types include “sandals”, “boots”, “sneakers”, “heels”, and “flats”. No specific footwear type inherently ranks above the other based solely on its category.

24. Digital Devices
Digital devices might be ranked when looking at website source data, for example. Some examples are “smartphones”, “tablets”, “laptops”, “desktop computers”, and “smartwatches”. There’s no inherent ranking or order among these devices based on their type.

25. Modes of Transportation
Modes of transportation might encompass “car”, “bicycle”, “train”, “airplane”, and “bus”. These modes don’t have an inherent order or superiority among them.

Other Types of Data in Research

Nominal data can be contrasted with other types of data, including ordinal, interval, and ratio data.

Here’s a short overview.

  • Ordinal data has categories, just like nominal data, but the categories in ordinal data can be logically ordered (Stockemer, 2018). These data would have an inherent ranking system. While ordinal data can be ordered, it differs from interval data (noted next) because there isn’t a quantifiable distance between different categories. For instance, t-shirt sizes (“small”, “medium”, and “large”) present an order but don’t clarify the extent of the difference between the categories. If we were to rank the sizes by inches around the waist, however, we would have interval data, discussed below (Katz, 2006a; Katz, 2006b).
  • Interval data, in contrast to nominal data, has ordered categories with consistent and quantifiable distances between each category. For example, temperature measurements such as Celsius or Fahrenheit represent interval data because we can measure the distance between 30 and 35 degrees using gauges (Lewis-Beck, Bryman & Liao, 2004).
  • Ratio data resembles interval data but with a defined zero point. The zero point represents the absence of the measured attribute. So, for example, zero on a Celsius scale doesn’t represent the absence of temperature, so it’s interval data. But the zero point of weight measurements (grams, kilograms) represents the absence of weight, so it’s ratio data (Katz, 2006a; Katz, 2006b). Ratio data allows for a greater range of statistical analyses, such as statements about how many times greater or smaller one value is compared to another.
  • Nominal data, explored in this article in detail, is data with categories that don’t have a natural order or ranking (Wilson & Joye, 2016). Unlike ordinal and interval data, nominal data does not provide any sense of hierarchy or order among the dataset.


Nominal variables are a form of categorical data. They enable researchers to differentiate data into distinctive groups or ‘buckets’ even when the data has no order or sequence. While these variables provide clear distinctions between categories, the lack of any order often limits the kind of statistical tests that can be applied to them. Nevertheless, they’re often used in descriptive research, such as for generating demographical data during cross-sectional studies.


Babbie, E., Halley, F., & Zaino, J. (2007). Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows (6th ed.). New York: SAGE Publications.

Hermans, K. (2023). Becoming an AI expert. Cybellium Ltd.

Katz, M. (2006). Study Design and Statistical Analysis: A Practical Guide for Clinicians. Cambridge: Cambridge University Press.

Katz, M. H. (2006). Multivariable analysis: A practical guide for clinicians. Cambridge: Cambridge University Press.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (Eds.). (2004). The SAGE Encyclopedia of Social Science Research Methods (Vol. 1). London: SAGE Publications.

Stockemer, D. (2018). Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata. London: Springer International Publishing.

Thiagarajan, B. (2023). Unlocking the Power of Data: A Beginner’s Guide to Data Analysis. Otolaryngology online.

Wilson, J. H., & Joye, S. W. (2016). Research Methods and Statistics: An Integrated Approach. New York: SAGE Publications.

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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