What Are Nominal Scale & Ordinal Scale Questions: A Guide With Examples

Every survey question serves a purpose, but not all questions are created equal. For example, a question like “Which social media platform do you use most often? (Facebook, Instagram, LinkedIn)” helps categorize data.

In contrast, a question like “Rank the following social media platforms in order of preference: Facebook, Instagram, LinkedIn.” introduces a hierarchy that can be analyzed.

Those are examples of nominal scale and ordinal scale questions, respectively—one helps identify data, and the other puts an order to it.

Choosing between these scales depends on what you want to learn and the type of data you’re handling. In this blog, I’ll break down their differences and provide practical examples to help you use them in a survey.

Let’s start with a tutorial on the basics of creating a survey first:

How to Create Engaging Surveys Online with ProProfs Survey Maker – Free & Easy

What Is a Nominal Scale and an Ordinal Scale?

Nominal scales classify data into categories without implying any order—think questions like “What’s your favorite color?” where responses are grouped but unordered.

Ordinal scales, on the other hand, rank responses in a meaningful sequence, such as rating satisfaction from 1 to 5, where the order matters but not the difference between levels.

Let’s discuss each in detail to get a better understanding of the subjects:

1. Nominal Scale

A nominal scale categorizes data into distinct groups or labels with no inherent order or ranking. These categories are used for identification and classification rather than comparison.

For example, if you ask, “What’s your favorite mode of transportation?” and provide options like car, bus, bike, and train, each response falls into a separate group without implying that one is better than the other.

Numbers can sometimes represent these categories (e.g., 1 for car, 2 for bus), but the values are purely symbolic, not indicative of rank.

This scale is best used when you need to sort data into groups, such as tracking demographic details or product preferences, without assigning any value beyond the category itself.

2. Ordinal Scale

An ordinal scale organizes data into a specific sequence or rank, where the order matters but the intervals between ranks are not uniform or measurable. For example, asking respondents to rate their satisfaction on a scale from “Very Satisfied” to “Very Dissatisfied” places their responses in a clear order but doesn’t measure or clarify the degree of difference between each level.

Ordinal data is useful when the focus is on the ranking itself, like prioritizing features in a product or assessing customer preferences. It adds an extra layer of depth compared to nominal data by introducing hierarchy while still being relatively simple to interpret.

What Are Some Examples of Nominal Survey Questions?

Nominal questions provide foundational data that’s essential for segmentation, market analysis, and understanding broad trends. They simplify the process of identifying patterns within specific categories, making them an invaluable tool for surveys across industries.

Here’s a wide range of nominal scale examples across various contexts:

1. Demographic Questions

• What is your marital status?
  • Single
  • Married
  • Divorced
  • Widowed
• What is your highest level of education?
  • High School
  • Associate Degree
  • Bachelor’s Degree
  • Master’s Degree
  • Doctorate
• What is your employment status?
  • Employed
  • Unemployed
  • Freelancer
  • Retired
  • Student
• What is your preferred language?
  • English
  • Spanish
  • French
  • Other

2. Preference-Based Questions

• Which streaming platform do you use the most?
  • Netflix
  • Hulu
  • Disney+
  • Amazon Prime
• What’s your favorite type of cuisine?
  • Italian
  • Chinese
  • Indian
  • Mexican
  • Other
• Which payment method do you prefer?
  • Credit Card
  • Debit Card
  • PayPal
  • Cryptocurrency
  • Other

Product or Service Feedback

• Which feature do you use most in our app?
  • Messaging
  • Notifications
  • Dashboard
  • Analytics
• Which department provided the best support?
  • Sales
  • Technical Support
  • Customer Service
  • Billing
• What is your preferred delivery method?
  • Standard Shipping
  • Express Shipping
  • In-store Pickup
  • Locker Pickup

Behavioral Questions

• What device do you primarily use for online shopping?
  • Smartphone
  • Laptop
  • Tablet
  • Desktop
• Which time of day do you usually exercise?
  • Morning
  • Afternoon
  • Evening
  • Night
• What type of content do you enjoy most online?
  • Blogs
  • Videos
  • Podcasts
  • Social Media Posts

Event or Experience Questions

• How did you hear about this event?
  • Social Media
  • Friend/Family Referral
  • Advertisement
  • Company Email
• Which genre of movies do you enjoy the most?
  • Action
  • Comedy
  • Drama
  • Thriller
• What’s your favorite type of vacation?
  • Beach Holiday
  • Adventure Travel
  • City Break
  • Cultural Experience

Healthcare & Lifestyle Questions

• What is your blood type?
  • A
  • B
  • AB
  • O
• Which fitness activity do you prefer?
  • Yoga
  • Running
  • Weightlifting
  • Cycling
• What’s your diet preference?
  • Vegan
  • Vegetarian
  • Pescatarian
  • Omnivore

Categorical Market Research

• Which car brand do you prefer?
  • Toyota
  • BMW
  • Tesla
  • Ford
• Which is your favorite online store?
  • Amazon
  • eBay
  • Walmart
  • Target

What Are Some Examples of Ordinal Survey Questions?

Ordinal questions provide actionable insights by capturing nuances in respondent preferences or attitudes. Whether you’re measuring satisfaction levels or prioritizing features, these questions help uncover trends and make data-driven decisions.

Here are a few ordinal scale examples:

Customer Satisfaction Questions

• How satisfied are you with our service?
  • Very Satisfied
  • Satisfied
  • Neutral
  • Dissatisfied
  • Very Dissatisfied
• Rate the ease of using our product:
  • Extremely Easy
  • Easy
  • Neutral
  • Difficult
  • Extremely Difficult
• How likely are you to recommend us to a friend?
  • Very Likely
  • Likely
  • Neutral
  • Unlikely
  • Very Unlikely

Preference Ranking Questions

• Rank the following features based on their importance to you:
  • Price
  • Durability
  • Design
  • Brand Reputation
• Order these vacation types from most to least preferred:
  • Adventure Travel
  • Relaxation at the Beach
  • Cultural Exploration
  • City Breaks

Behavioral Frequency Questions

• How often do you use our service?
  • Daily
  • Weekly
  • Monthly
  • Occasionally
  • Never
• How frequently do you engage with our app notifications?
  • Always
  • Often
  • Sometimes
  • Rarely
  • Never

Experience or Event Feedback

• Rate your overall experience at our event:
  • Excellent
  • Good
  • Neutral
  • Poor
  • Terrible
• How would you describe the quality of the catering?
  • Outstanding
  • Good
  • Average
  • Below Average
  • Poor

Educational or Training Feedback

• Rate the usefulness of this training session:
  • Extremely Useful
  • Useful
  • Neutral
  • Not Very Useful
  • Not Useful at All
• How confident are you about applying what you’ve learned?
  • Very Confident
  • Confident
  • Neutral
  • Somewhat Confident
  • Not Confident

Healthcare and Wellness Feedback

• Rate the effectiveness of your recent treatment:
  • Highly Effective
  • Effective
  • Neutral
  • Ineffective
  • Very Ineffective
• How would you rate your current level of physical activity?
  • Very Active
  • Active
  • Moderately Active
  • Rarely Active
  • Not Active

Workplace or Employee Engagement Surveys

• How satisfied are you with your work-life balance?
  • Very Satisfied
  • Satisfied
  • Neutral
  • Dissatisfied
  • Very Dissatisfied
• Rate the communication within your team:
  • Excellent
  • Good
  • Neutral
  • Poor
  • Terrible

Market Research and Consumer Behavior

• How important is sustainability when purchasing products?
  • Extremely Important
  • Very Important
  • Neutral
  • Somewhat Important
  • Not Important at All
• Rate your interest in trying new product releases:
  • Very Interested
  • Interested
  • Neutral
  • Uninterested
  • Very Uninterested

What Are the Characteristics of Nominal Scale & Ordinal Scale?

Both nominal and ordinal scales are types of measurement scales used to categorize data. However, they differ in the level of information they provide. Here’s a breakdown of their characteristics:

1. Nominal Scale

• Categorization: This is the most basic level of measurement. Nominal scales are used to categorize data into distinct, mutually exclusive groups or categories.
• No Order or Ranking: The categories have no inherent order or ranking. One category isn’t considered superior or inferior to another. They are simply different.
• Qualitative Data: Nominal scales deal with qualitative data, focusing on the attributes or qualities of the data.
• Examples:
  • Gender: Male, Female, Other
  • Eye color: Blue, Brown, Green
  • Types of fruit: Apple, Banana, Orange
  • Zip codes: While numeric, they represent geographic areas, not a quantitative value.
• Analysis: You can calculate the frequency of each category (how many fall into each group), the mode (the most frequent category), and use percentages to describe the distribution.
• Mathematical Operations: You cannot perform meaningful mathematical operations like addition, subtraction, multiplication, or division on nominal scale data.

2. Ordinal Scale

• Categorization and Order: Like nominal scales, ordinal scales categorize data. However, they also introduce an order or ranking among the categories.
• Relative Position: Ordinal scales indicate the relative position of items but not the magnitude of difference between them. We know one category is higher or lower than another, but not by how much.
• Examples:
  • Education level: High School, Bachelor’s, Master’s, PhD
  • Socioeconomic status: Low, Middle, High
  • Customer satisfaction: Very Dissatisfied, Dissatisfied, Neutral, Satisfied, Very Satisfied
  • Likert scales: Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree
• Analysis: In addition to frequency, mode, and percentages, you can also determine the median (the middle value) and percentiles with ordinal data. You can also use non-parametric statistical tests designed for ranked data.
• Mathematical Operations: While you can determine order, you cannot perform standard mathematical operations on ordinal scale data because the intervals between categories are not known or equal.

Key Characteristics in a Nutshell:

What Are the Major Differences Between Ordinal vs. Nominal Scale?

While both nominal and ordinal scales categorize data, they differ significantly in the information they convey. Here’s a detailed look at their differences:  

Nature of Categorization

• Nominal Scale: Focuses purely on naming and classifying data into distinct, non-overlapping categories. There’s no inherent order or hierarchy among these categories. Think of it like assigning labels.  
• Ordinal Scale: Goes beyond mere categorization by introducing an order or ranking among the categories. This order indicates the relative position, telling us which category is higher or lower than another.  

Magnitude of Difference

• Nominal Scale: The concept of “difference” between categories isn’t applicable. Categories are simply different, not greater or lesser than each other.
• Ordinal Scale: While it establishes order, the magnitude of difference between ranks remains unknown and likely unequal. We know one category is higher, but not by how much. The intervals between ranks are not defined or consistent.  

Data Type and Interpretation

• Nominal Scale: Deals with qualitative data, representing attributes, qualities, or characteristics. Interpretation focuses on the “what” – what category an observation belongs to.  
• Ordinal Scale: Also handles qualitative data, but with the added dimension of order. Interpretation considers both the “what” and the “relative position” – what category and where it stands in the ranking.

Permissible Statistical Analysis

• Nominal Scale: Limits analysis to frequencies, percentages, and mode. You can count how many fall into each category, determine the most frequent category, and express proportions.  
• Ordinal Scale: Allows for more extensive analysis, including median and percentiles, in addition to the analyses possible with nominal data. You can find the middle value and describe the data’s distribution in terms of ranked positions. Non-parametric statistical tests designed for ranked data are also applicable.  

Mathematical Operations

• Nominal Scale: Meaningful mathematical operations (addition, subtraction, etc.) are not possible because the categories lack numerical value.  
• Ordinal Scale: While you can determine order, standard mathematical operations are inappropriate because the intervals between ranks are undefined and likely unequal. Calculating the mean (average) of ordinal data is a common statistical error.

Illustrative Examples Highlighting the Differences

Why Understanding the Difference Matters

Accurately identifying the level of measurement is crucial for choosing appropriate statistical analysis methods. Misinterpreting an ordinal scale as an interval or ratio can lead to flawed analysis and misleading conclusions.

For example, calculating the average of Likert scale responses (ordinal) is statistically invalid because the distances between “strongly agree” and “agree” are not necessarily the same as between “agree” and “neutral.”  

By recognizing the distinctions between nominal and ordinal scales, you ensure the integrity and validity of your data analysis and interpretation.

Differences Between Ordinal vs. Nominal Scale at a Quick Glance:

How to Craft Effective Nominal & Ordinal Survey Questions

Crafting effective nominal and ordinal survey questions is crucial for gathering meaningful data and drawing accurate conclusions. Here’s a breakdown of how to design each type of question:

For Nominal Survey Questions

Nominal questions aim to categorize respondents or their responses into distinct groups. Here’s how to make them effective:

• Ensure Mutually Exclusive Categories: Each response option should be clearly distinct from the others, with no overlap. A respondent should fit into only one category.
  • Example:
    • Poor: What is your age? (18-25, 25-35, 35-45) (overlap at 25 and 35)
    • Good: What is your age? (18-24, 25-34, 35-44)
• Provide Exhaustive Options: Include all possible relevant categories to ensure everyone can find a suitable response. Consider an “Other” option with an open-ended text field if you’re unsure about capturing all possibilities.
  • Example:
    • Poor: What is your primary mode of transportation to work? (Car, Bus)
    • Good: What is your primary mode of transportation to work? (Car, Bus, Train, Bicycle, Walk, Other: _______)
• Use Clear and Concise Language: Keep the question and response options simple and easy to understand. Avoid jargon or technical terms.
  • Example:
    • Poor: What is your preferred pecuniary instrument for quotidian transactions?
    • Good: What is your preferred payment method for everyday purchases?
• Avoid Leading or Biased Questions: The question should not steer respondents towards a particular answer.
  • Example:
    • Poor: Do you agree that our amazing new product is superior to the competition?
    • Good: How would you rate our new product compared to similar products you’ve used?

For Ordinal Survey Questions

Ordinal questions introduce order or ranking to the response categories. Here’s how to optimize them:

• Establish a Clear Order: The response options should have a logical and easily understood order or progression.
  • Example:
    • Good: How satisfied are you with our customer service? (Very Dissatisfied, Dissatisfied, Neutral, Satisfied, Very Satisfied)
• Use Balanced Scales: Provide an equal number of positive and negative options with a neutral midpoint whenever possible.
  • Example:
    • Poor: How would you rate the ease of using our website? (Difficult, Somewhat Difficult, Easy, Very Easy)
    • Good: How would you rate the ease of using our website? (Very Difficult, Difficult, Neutral, Easy, Very Easy)
• Consider the Number of Response Options: Too few options limit sensitivity, while too many can be overwhelming. 5-7 options are generally effective.
• Provide Meaningful Anchors: Clearly define the meaning of the endpoints of your scale to provide context and improve consistency in interpretation.
  • Example:
    • Poor: How important is price when choosing a restaurant? (Not Important, Important, Very Important)
    • Good: When choosing a restaurant, how important is price to you?
      • 1 (Not at all Important): Price is not a factor in my decision.
      • 5 (Extremely Important): Price is the most important factor in my decision.
• Maintain Consistency in Ordering: Use the same order direction (e.g., increasing or decreasing) throughout the survey to avoid confusion.

General Tips for Both Nominal and Ordinal Questions

• Keep it Concise: Avoid lengthy or complex questions that may confuse respondents.
• Pilot Test Your Questions: Test your questions on a small group before launching the full survey to identify any issues with clarity, ambiguity, or response options.
• Consider Visual Aids: Use visual scales or progress bars to enhance engagement and understanding, especially for ordinal questions.

By following these guidelines, you can create nominal and ordinal survey questions that yield reliable data and valuable insights for your research or business objectives.

Survey Design Best Practices

Designing a survey that yields accurate and insightful data requires careful planning and attention to detail. Here are some best practices to keep in mind:

1. Define Clear Objectives

• Start with a Purpose: What do you want to achieve with this survey? Clearly define your research questions and goals before you start designing. This will guide your question selection and ensure you collect relevant data.

2. Know Your Target Audience

• Who are you surveying? Tailor your questions and language to the demographics, knowledge level, and interests of your target audience. This ensures better comprehension and response rates.

Structure for Flow and Engagement

• Logical Ordering: Organize questions logically, moving from general to specific or from less sensitive to more sensitive topics. This helps maintain respondent interest and prevent confusion.
• Group Similar Questions: Group questions on the same topic together to improve flow and coherence.
• Use Skip Logic and Branching: Employ skip logic and branching to direct respondents to relevant questions based on previous answers. This creates a more personalized and efficient experience.

Question Wording and Design

• Clarity is Key: Use clear, concise, and unambiguous language. Avoid jargon, technical terms, or leading questions that might bias responses.
• Keep it Brief: Shorter surveys tend to have higher completion rates. Only include essential questions and avoid unnecessary detail.
• Variety in Question Types: Use a mix of question types (multiple-choice, rating scales, open-ended) to keep respondents engaged and gather diverse data.
• Offer Balanced Scales: For rating scales, provide a balanced number of positive and negative options with a clear neutral point.
• Consider Visual Aids: Use visual scales, progress bars, or images to enhance engagement and understanding.

Response Options

• Mutually Exclusive and Exhaustive: Ensure response options are mutually exclusive (no overlap) and exhaustive (cover all possibilities). Include an “Other” option when necessary.
• Limit Open-Ended Questions: While valuable for qualitative insights, too many open-ended questions can be time-consuming and reduce response rates. Use them strategically.

Survey Length and Appearance

• Keep it Concise: Aim for a survey that can be completed in a reasonable time frame (ideally under 10 minutes).
• Visually Appealing: Use a clean and professional design with clear formatting and fonts.

Pilot Testing and Refinement

• Test Before Launch: Always pilot-test your survey with a small group to identify any issues with clarity, flow, or question design.
• Iterate and Improve: Use feedback from the pilot test to refine your survey before launching it to a wider audience.

Ethical Considerations

• Informed Consent: Clearly inform respondents about the survey’s purpose and how their data will be used to ensure voluntary participation.
• Data Privacy and Security: Protect respondent confidentiality and ensure data security.

Analyze Responses With a Survey Tool

• Choose an Appropriate Tool: Use a reliable survey tool with built-in analytics and reporting to collect and analyze your survey data easily. I’d recommend a tool like ProProfs Survey Maker, which not only analyzes data but presents the findings in an easily interpretable format.
• Present Clear Findings: Communicate your survey results in a clear and concise manner, using visuals and charts to aid understanding.

Improve Research Quality With the Right Nominal Scale & Ordinal Scale Questions

Nominal and ordinal scales are foundational tools for survey design, each serving distinct roles in categorizing and ranking data. Whether conducting market research or collecting feedback, mastering these scales ensures your data collection efforts hit the mark.

The first step is to choose a tool that allows you to employ a wide range of question types and has robust analytics to interpret the data accurately.

ProProfs Survey Maker comes to mind for more reason than one – including both these aforementioned features along with a horde of others like AI surveys, intuitive user interface, readymade templates, skip and branching logic, and more. 

Happy surveying!