what is ordinal data in research

What Is Ordinal Data?

What is ordinal data, how is it used, and how do you collect and analyze it? Find out in this comprehensive guide.

Whether you’re new to data analytics or simply need a refresher on the fundamentals, a key place to start is with the four types of data. Also known as the four levels of measurement , this data analytics term describes the level of detail and precision with which data is measured. The four types (or scales) of data are:

• nominal data
• ordinal data
• interval data

In this article, I’m going to dive deep into ordinal data.

If the concept of these data types is completely new to you, we’ll start with a quick summary of the four different types, and then explore the various aspects of ordinal data in a bit more detail,

If you’d like to learn more data analytics skills, try our free 5-day data short course .

I’ll cover the following topics:

• An introduction to the four different types of data
• What is ordinal data? A definition

What are some examples of ordinal data?

• How is ordinal data collected and what is it used for?
• How to analyze ordinal data
• Summary and further reading

Ready to get your head around ordinal data? Then let’s get going!

1. An introduction to the four different types of data

To analyze a dataset, you first need to determine what type of data you’re dealing with.

Fortunately, to make this easier, all types of data fit into one of four broad categories: nominal , ordinal , interval, and ratio data. While these are commonly referred to as ‘data types,’ they are really different scales or levels of measurement .

Each level of measurement indicates how precisely a variable has been counted, determining the methods you can use to extract information from it. The four data types are not always clearly distinguishable; rather, they belong to a hierarchy. Each step in the hierarchy builds on the one before it.

The first two types of data, known as categorical data , are nominal and ordinal. These two scales take relatively imprecise measures.

While this makes them easier to analyze, it also means they offer less accurate insights. The next two types of data are interval and ratio. These are both types of numerical data , which makes them more complex. They are more difficult to analyze but have the potential to offer much richer insights.

• Nominal data is the simplest data type. It classifies data purely by labeling or naming values e.g. measuring marital status, hair, or eye color. It has no hierarchy to it.
• Ordinal data classifies data while introducing an order, or ranking. For instance, measuring economic status using the hierarchy: ‘wealthy’, ‘middle income’ or ‘poor.’ However, there is no clearly defined interval between these categories.
• Interval data classifies and ranks data but also introduces measured intervals. A great example is temperature scales, in Celsius or Fahrenheit. However, interval data has no true zero, i.e. a measurement of ‘zero’ can still represent a quantifiable measure (such as zero Celsius, which is simply another measure on a scale that includes negative values).
• Ratio data is the most complex level of measurement. Like interval data, it classifies and ranks data, and uses measured intervals. However, unlike interval data, ratio data also has a true zero. When a variable equals zero, there is none of this variable. A good example of ratio data is the measure of height—you cannot have a negative measure of height.

You’ll find a comprehensive guide to the four levels of data measurement here .

What do the different levels of measurement tell you?

Distinguishing between the different levels of measurement is sometimes a little tricky.

However, it’s important to learn how to distinguish them, because the type of data you’re working with determines the statistical techniques you can use to analyze it. Data analysis involves using descriptive analytics (to summarize the characteristics of a dataset) and inferential statistics (to infer meaning from those data).

These comprise a wide range of analytical techniques, so before collecting any data, you should decide which level of measurement is best for your intended purposes.

2. What is ordinal data? A definition

Ordinal data is a type of qualitative (non-numeric) data that groups variables into descriptive categories.

A distinguishing feature of ordinal data is that the categories it uses are ordered on some kind of hierarchical scale, e.g. high to low. On the levels of measurement, ordinal data comes second in complexity, directly after nominal data.

While ordinal data is more complex than nominal data (which has no inherent order) it is still relatively simplistic.

For instance, the terms ‘wealthy’, ‘middle income’, and ‘poor’ may give you a rough idea of someone’s economic status, but they are an imprecise measure–there is no clear interval between them. Nevertheless, ordinal data is excellent for ‘sticking a finger in the wind’ if you’re taking broad measures from a sample group and fine precision is not a requirement.

While ordinal data is non-numeric, it’s important to understand that it can still contain numerical figures. However, these figures can only be used as categorizing labels, i.e. they should have no inherent mathematical value.

For instance, if you were to measure people’s economic status you could use number 3 as shorthand for ‘wealthy’, number 2 for ‘middle income’, and number 1 for ‘poor.’ At a glance, this might imply numerical value, e.g. 3 = high and 1 = low. However, the numbers are only used to denote sequence. You could just as easily switch 3 with 1, or with ‘A’ and ‘B’ and it would not change the value of what you’re ordering; only the labels used to order it.

Key characteristics of ordinal data

• Ordinal data are categorical (non-numeric) but may use numbers as labels.
• Ordinal data are always placed into some kind of hierarchy or order (hence the name ‘ordinal’—a good tip for remembering what makes it unique!)
• While ordinal data are always ranked, the values do not have an even distribution .
• Using ordinal data, you can calculate the following summary statistics: frequency distribution, mode and median, and the range of variables.

What’s the difference between ordinal data and nominal data?

While nominal and ordinal data are both types of non-numeric measurement, nominal data have no order or sequence.

For instance, nominal data may measure the variable ‘marital status,’ with possible outcomes ‘single’, ‘married’, ‘cohabiting’, ‘divorced’ (and so on). However, none of these categories are ‘less’ or ‘more’ than any other. Another example might be eye color. Meanwhile, ordinal data always has an inherent order.

If a qualitative dataset lacks order, you know you’re dealing with nominal data.

3. What are some examples of ordinal data?

• Economic status (poor, middle income, wealthy)
• Income level in non-equally distributed ranges ($10K-$20K, $20K-$35K, $35K-$100K)
• Course grades (A+, A-, B+, B-, C)
• Education level (Elementary, High School, College, Graduate, Post-graduate)
• Likert scales (Very satisfied, satisfied, neutral, dissatisfied, very dissatisfied)
• Military ranks (Colonel, Brigadier General, Major General, Lieutenant General)
• Age (child, teenager, young adult, middle-aged, retiree)

As is hopefully clear by now, ordinal data is an imprecise but nevertheless useful way of measuring and ordering data based on its characteristics. Next up, let’s see how ordinal data is collected and how it generally tends to be used.

4. How is ordinal data collected and what is it used for?

Ordinal data are usually collected via surveys or questionnaires. Any type of question that ranks answers using an explicit or implicit scale can be used to collect ordinal data. An example might be:

• Question: Which best describes your knowledge of the Python programming language? Possible answers: Beginner, Basic, Intermediate, Advanced, Expert.

This commonly recognized type of ordinal question uses the Likert Scale, which we described briefly in the previous section. Another example might be:

• Question: To what extent do you agree that data analytics is the most important job for the 21st century? Possible answers: Strongly agree, Agree, Neutral, Disagree, Strongly Disagree.

It’s worth noting that the Likert Scale is sometimes used as a form of interval data. However, this is strictly incorrect. That’s because Likert Scales use discrete values , while interval data uses continuous values with a precise interval between them.

The distinctions between values on an ordinal scale, meanwhile, lack clear definition or separation, i.e. they are discrete. Although this means the values are imprecise and do not offer granular detail about a population, they are an excellent way to draw easy comparisons between different values in a sample group.

How is ordinal data used?

Ordinal data are commonly used for collecting demographic information.

This is particularly prevalent in sectors like finance, marketing, and insurance, but it is also used by governments, e.g. the census, and is generally common when conducting customer satisfaction surveys (in any industry).

5. How to analyze ordinal data

As discussed, the level of measurement you use determines the kinds of analysis you can carry out on your data. In general, these fall into two broad categories: descriptive statistics and inferential statistics.

We use descriptive statistics to summarize the characteristics of a dataset. This helps us spot patterns. Meanwhile, inferential statistics allow us to make predictions (or infer future trends) based on existing data. However, depending on the measurement scale, there are limits. You can learn more about the difference between descriptive and inferential statistics here .

For now, though, Let’s see what kinds of descriptive and inferential statistics you can measure using ordinal data.

Descriptive statistics for ordinal data

The descriptive statistics you can obtain using ordinal data are:

Frequency distribution

Measures of central tendency: mode and/or median, measures of variability: range.

Now let’s look at each of these in more depth.

Frequency distribution describes how your ordinal data are distributed.

For instance, let’s say you’ve surveyed students on what grade they’ve received in an examination. Possible grades range from A to C. You can summarize this information using a pivot table or frequency table, with values represented either as a percentage or as a count. To illustrate using a very simple example, one such table might look like this:

As you can see, the values in the sum column show how many students received each possible grade. This allows you to see how the values are distributed. Another option is also to visualize the data , for instance using a bar plot.

Viewing the data visually allows us to easily see the frequency distribution. Note the hierarchical relationship between categories. This is different from the other type of categorical data, nominal data, which lacks any hierarchy.

The mode (the value which is most often repeated) and median (the central value) are two measures of what is known as ‘central tendency.’ There is also a third measure of central tendency: the mean. However, because ordinal data is non-numeric, it cannot be used to obtain the mean. That’s because identifying the mean requires mathematical operations that cannot be meaningfully carried out using ordinal data.

However, it is always possible to identify the mode in an ordinal dataset. Using the barplot or frequency table, we can easily see that the mode of the different grades is B. This is because B is the grade that most students received.

In this case, we can also identify the median value. The median value is the one that separates the top half of the dataset from the bottom half. If you imagined all the respondents’ answers lined up end-to-end, you could then identify the central value in the dataset. With 165 responses (as in our grades example) the central value is the 83rd one. This falls under the grade B.

The range is one measure of what is known as ‘variability.’ Other measures of variability include variance and standard deviation. However, it is not possible to measure these using ordinal data, for the same reasons you cannot measure the mean.

The range describes the difference between the smallest and largest value. To calculate this, you first need to use numeric codes to represent each grade, i.e. A = 1, A- = 2, B = 3, etc. The range would be 5 – 1 = 4. So in this simple example, the range is 4. This is an easy calculation to carry out. The range is useful because it offers a basic understanding of how spread out the values in a dataset are.

Inferential statistics for ordinal data

Descriptive statistics help us summarize data. To infer broader insights, we need inferential statistics. Inferential statistics work by testing hypotheses and drawing conclusions based on what we learn.

There are two broad types of techniques that we can use to do this. Parametric and non-parametric tests. For qualitative (rather than quantitative) data like ordinal and nominal data, we can only use non-parametric techniques.

Non-parametric approaches you might use on ordinal data include:

Mood’s median test

• The Mann-Whitney U test

Wilcoxon signed-rank test

• The Kruskal-Wallis H test:

Spearman’s rank correlation coefficient

Let’s briefly look at these now.

The Mood’s median test lets you compare medians from two or more sample populations in order to determine the difference between them. For example, you may wish to compare the median number of positive reviews of a company on Trustpilot versus the median number of negative reviews. This will help you determine if you’re getting more negative or positive reviews.

The Mann-Whitney U-test

The Mann-Whitney U test lets you compare whether two samples come from the same population.

It can also be used to identify whether or not observations in one sample group tend to be larger than observations in another sample. For example, you could use the test to understand if salaries vary based on age. Your dependent variable would be ‘salary’ while your independent variable would be ‘age’, with two broad groups, e.g. ‘under 30,’ ‘over 60.’

The Wilcoxon signed-rank test explores the distribution of scores in two dependent data samples (or repeated measures of a single sample) to compare how, and to what extent, the mean rank of their populations differs.

We can use this test to determine whether two samples have been selected from populations with an equal distribution or if there is a statistically significant difference.

The Kruskal-Wallis H test

The Kruskal-Wallis H test helps us to compare the mean ranking of scores across three or more independent data samples.

It’s an extension of the Mann-Whitney U test that increases the number of samples to more than two. In the Kruskal-Wallis H test, samples can be of equal or different sizes. We can use it to determine if the samples originate from the same distribution.  

Spearman’s rank correlation coefficient explores possible relationships (or correlations) between two ordinal variables.

Specifically, it measures the statistical dependence between those variable’s rankings. For instance, you might use it to compare how many hours someone spends a week on social media versus their IQ. This would help you to identify if there is a correlation between the two.

Don’t worry if these models are complex to get your head around. At this stage, you just need to know that there are a wide range of statistical methods at your disposal. While this means there is lots to learn, it also offers the potential for obtaining rich insights from your data.

6. Summary and further reading

In this guide, we:

• Introduced the four levels of data measurement: Nominal, ordinal, interval, and ratio.
• Defined ordinal data as a qualitative (non-numeric) data type that groups variables into ranked descriptive categories.
• Explained the difference between ordinal and nominal data: Both are types of categorical data. However, nominal data lacks hierarchy, whereas ordinal data ranks categories using discrete values with a clear order.
• Shared some examples of nominal data: Likert scales, education level, and military rankings.
• Highlighted the descriptive statistics you can obtain using ordinal data: Frequency distribution, measures of central tendency (the mode and median), and variability (the range).
• Introduced some non-parametric statistical tests for analyzing ordinal data, e.g. Mood’s median test and the Kruskal-Wallis H test.

Want to learn more about data analytics or statistics? To further develop your understanding, check out our  free-five day data analytics short course and read the following guides:

• What is quantitative data?
• An introduction to exploratory data analysis
• An introduction to multivariate data analysis

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Ordinal Data Explained: Definition, Examples, and Analysis

Discover the power of ordinal data in research and analytics. learn its definition, examples, collection methods, and analysis techniques to enhance your data-driven decision making., short on time get instant insights with an ai summary of this post., introduction.

In the world of data analysis and research, understanding different types of data is crucial for making informed decisions. One such important category is ordinal data, which plays a significant role in various fields, from market research to social sciences. Let's dive into what ordinal data is, why it matters, and how it fits into the broader spectrum of statistical data types.

What is Ordinal Data?

Ordinal data is a type of categorical data where the categories have a natural, meaningful order or ranking, but the differences between the categories are not necessarily equal or measurable. It's like a middle ground between nominal data (categories with no inherent order) and interval/ratio data (where differences are quantifiable).

For example, think about customer satisfaction ratings:

• Very Dissatisfied
• Dissatisfied
• Very Satisfied

Here, we can clearly see an order from least to most satisfied, but we can't say that the difference between "Dissatisfied" and "Neutral" is the same as the difference between "Neutral" and "Satisfied."

Why Ordinal Data Matters

Ordinal data is incredibly valuable in research and business for several reasons:

Captures Nuanced Information : It allows researchers to collect more detailed information than simple yes/no questions, providing a richer understanding of opinions and preferences.

Easy for Respondents : People often find it easier to express their views on a scale rather than giving precise numerical values.

Versatile Analysis : While not as mathematically flexible as interval or ratio data, ordinal data still allows for meaningful statistical analysis and interpretation.

Common in Real-World Scenarios : Many real-life situations naturally lend themselves to ordinal categorization, making it a practical choice for various studies.

The Four Statistical Data Types

To fully appreciate ordinal data, it's helpful to understand how it fits into the broader context of statistical data types. There are four main categories:

Nominal Data : Categories with no inherent order (e.g., colors, gender).

Ordinal Data : Ordered categories where the differences between categories are not necessarily equal (e.g., education levels, Likert scales).

Interval Data : Ordered categories with equal intervals between values, but no true zero point (e.g., temperature in Celsius).

Ratio Data : Similar to interval data, but with a true zero point (e.g., height, weight, age).

Understanding these distinctions is crucial for choosing the right data collection methods and analysis techniques. For instance, tools like Innerview can help researchers efficiently gather and analyze various types of data, including ordinal data, from user interviews and surveys. By leveraging such advanced tools, teams can save time on transcription and analysis, allowing them to focus on extracting meaningful insights from their ordinal data sets.

As we continue to explore ordinal data, we'll delve deeper into its applications, collection methods, and analysis techniques, equipping you with the knowledge to make the most of this valuable data type in your research and decision-making processes.

Discover more insights in: Comprehensive Guide to Research Types: From Fundamental to Applied

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Understanding Ordinal Data

Now that we've established what ordinal data is and its importance in the broader context of statistical data types, let's explore its key characteristics, how it differs from other data types, and examine some real-world examples across various fields.

Key Characteristics of Ordinal Data

Ordinal data has several unique features that set it apart:

Ordered Categories : The most defining characteristic of ordinal data is that its categories have a clear, meaningful order or ranking.

Non-Quantifiable Intervals : While the categories are ordered, the differences between them aren't necessarily equal or measurable.

Limited Mathematical Operations : You can use median and mode with ordinal data, but mean calculations aren't meaningful due to the non-quantifiable intervals.

Ranking Without Precise Measurement : Ordinal data allows for comparison and ranking without requiring exact numerical values.

Flexibility in Response Options : It often uses scales (like Likert scales) that offer a range of options, allowing for nuanced responses.

Ordinal Data vs. Other Data Types

To better understand ordinal data, let's compare it to other data types:

Ordinal vs. Nominal Data

• Ordinal : Has a natural order (e.g., education levels: high school, bachelor's, master's, doctorate)
• Nominal : No inherent order (e.g., favorite colors: red, blue, green)

Ordinal vs. Interval Data

• Ordinal : Order exists, but differences aren't quantifiable (e.g., movie ratings: 1 star, 2 stars, 3 stars)
• Interval : Order exists with quantifiable, equal intervals (e.g., temperature in Celsius)

Ordinal vs. Ratio Data

• Ordinal : No true zero point (e.g., satisfaction levels: very unsatisfied to very satisfied)
• Ratio : Has a true zero point (e.g., age, weight, height)

Examples of Ordinal Data in Various Fields

Ordinal data is prevalent across numerous disciplines. Here are some examples:

Marketing Research :

• Customer satisfaction surveys (Very Unsatisfied to Very Satisfied)
• Brand preference rankings (1st choice, 2nd choice, 3rd choice)
• Product quality ratings (Poor, Fair, Good, Excellent)

Education :

• Letter grades (A, B, C, D, F)
• Academic degrees (Associate's, Bachelor's, Master's, Doctorate)
• Proficiency levels (Beginner, Intermediate, Advanced, Expert)

Healthcare :

• Pain scales (No Pain, Mild, Moderate, Severe)
• Triage systems (Immediate, Urgent, Semi-urgent, Non-urgent)
• Disease staging (Stage I, Stage II, Stage III, Stage IV)

Social Sciences :

• Socioeconomic status (Lower class, Middle class, Upper class)
• Likert scale responses (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree)
• Frequency of behavior (Never, Rarely, Sometimes, Often, Always)

Business and Management :

• Employee performance ratings (Needs Improvement, Meets Expectations, Exceeds Expectations)
• Project priority levels (Low, Medium, High, Critical)
• Risk assessment categories (Low Risk, Moderate Risk, High Risk)

Understanding these examples and characteristics of ordinal data is crucial for effective data collection and analysis. Tools like Innerview can be particularly helpful when working with ordinal data in user research contexts. By leveraging AI-powered analysis and customizable views, Innerview allows researchers to quickly identify patterns and extract insights from ordinal data sets, significantly reducing analysis time and enhancing the depth of insights gained from user interviews and surveys.

As we continue to explore ordinal data, we'll look at effective collection methods and analysis techniques that can help you make the most of this valuable data type in your research and decision-making processes.

Collecting Ordinal Data

Collecting ordinal data is a crucial step in many research processes, from market surveys to academic studies. The methods you use to gather this data can significantly impact the quality and usefulness of your results. Let's explore some effective strategies for collecting ordinal data, with a focus on survey design, best practices, and the popular Likert scale.

Survey and Questionnaire Design for Ordinal Data

When designing surveys to collect ordinal data, it's essential to create questions that naturally lend themselves to ordered responses. Here are some key considerations:

Clear and Concise Questions : Ensure your questions are easy to understand and directly related to what you're trying to measure.

Appropriate Response Options : Provide a range of options that cover the full spectrum of possible answers, with a clear order from lowest to highest.

Balanced Scales : Use an equal number of positive and negative options to avoid bias. For example: Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree.

Consistent Format : Maintain consistency in the format and wording of your questions and response options throughout the survey.

Avoid Double-Barreled Questions : Each question should focus on one aspect or attribute to prevent confusion and ensure accurate responses.

Best Practices for Creating Ordinal Scales

To create effective ordinal scales, consider these best practices:

Choose the Right Number of Options : While there's no one-size-fits-all approach, 5 to 7 options often provide a good balance between precision and ease of use.

Use Clear Labels : Each point on your scale should have a clear, unambiguous label that respondents can easily understand.

Consider Your Audience : Tailor your language and scale complexity to your target respondents. For example, you might use simpler scales for children or non-expert audiences.

Test Your Scale : Before launching your full survey, pilot test your questions and scales with a small group to identify any issues or confusion.

Provide Context : If necessary, include brief explanations or examples to help respondents understand what each point on the scale represents.

Consider Including a Neutral Option : This can be important for respondents who truly have no opinion, but be aware that it may also encourage non-committal responses.

Likert Scale: A Common Ordinal Data Collection Tool

The Likert scale, developed by psychologist Rensis Likert, is one of the most widely used tools for collecting ordinal data. It typically consists of a statement followed by a 5 or 7-point scale ranging from strong disagreement to strong agreement.

Here's an example of a 5-point Likert scale:

• Strongly Disagree
• Neither Agree nor Disagree
• Strongly Agree

Likert scales are popular because they're:

• Easy to Understand : Respondents generally find them intuitive and straightforward to use.
• Versatile : They can be used to measure attitudes, opinions, and perceptions across various topics.
• Quantifiable : While still ordinal, Likert scale data can be analyzed using various statistical methods.
• Comparable : They allow for easy comparison of responses across different questions or surveys.

When using Likert scales, it's important to:

• Balance Positive and Negative Statements : This helps prevent response bias.
• Consider Cultural Differences : Some cultures may interpret scales differently, so be mindful when conducting international research.
• Use Consistent Labeling : Keep the order and wording of your scale consistent throughout your survey.

By following these guidelines and best practices, you can design effective surveys and questionnaires that capture high-quality ordinal data. Tools like Innerview can further enhance your data collection process by providing AI-powered analysis of your survey responses, helping you quickly identify patterns and extract meaningful insights from your ordinal data sets. This can be particularly useful when dealing with large volumes of survey data or when conducting user interviews where ordinal scales are used to gauge user opinions and preferences.

Discover more insights in: Qualitative vs Quantitative Research: Understanding the Key Differences

Applications of Ordinal Data

Ordinal data isn't just a theoretical concept - it's a powerful tool with real-world applications across various industries. Let's explore how different sectors leverage ordinal data to gain insights and make informed decisions.

Marketing and Customer Research

In the world of marketing and customer research, ordinal data is a goldmine of information. Companies use it to:

• Measure Customer Satisfaction : Using scales from "Very Dissatisfied" to "Very Satisfied," businesses can gauge how happy their customers are with products or services.
• Assess Brand Perception : Ordinal scales help marketers understand how consumers view their brand compared to competitors.
• Evaluate Product Features : By ranking features from "Least Important" to "Most Important," companies can prioritize development efforts.

For example, a smartphone manufacturer might use a Likert scale to assess customer satisfaction with different aspects of their latest model:

• Battery Life
• Camera Quality
• Processing Speed
• Design Aesthetics
• User Interface

By analyzing this ordinal data, the company can identify areas for improvement and make data-driven decisions for future product development.

Medical Studies and Clinical Trials

In healthcare, ordinal data plays a crucial role in:

• Pain Assessment : The widely used pain scale (0-10) helps medical professionals understand and track a patient's pain levels over time.
• Disease Progression : Ordinal scales are used to categorize the stages of diseases, such as cancer (Stage I, II, III, IV).
• Treatment Efficacy : Researchers use ordinal data to measure the effectiveness of treatments, from "No Improvement" to "Significant Improvement."

For instance, in a clinical trial for a new arthritis medication, researchers might use an ordinal scale to measure changes in patients' mobility:

• No Mobility
• Limited Mobility
• Moderate Mobility
• Good Mobility
• Full Mobility

This ordinal data allows for a nuanced understanding of the medication's effects, which might be lost with simple yes/no questions.

Educational Assessments and Evaluations

The education sector relies heavily on ordinal data for:

• Grading Systems : The classic A, B, C, D, F scale is a perfect example of ordinal data in education.
• Skill Assessments : Language proficiency tests often use ordinal scales (Beginner, Intermediate, Advanced, Fluent).
• Course Evaluations : Students frequently rate their courses and instructors using ordinal scales.

Consider a university using a 5-point scale for student course evaluations:

• Below Average
• Above Average

This ordinal data helps administrators identify standout instructors and courses that may need improvement.

Financial Services and Economics

In the world of finance and economics, ordinal data helps in:

• Credit Ratings : Agencies use ordinal scales (AAA, AA, A, BBB, etc.) to rate the creditworthiness of companies and countries.
• Investment Risk Assessment : From "Low Risk" to "High Risk," these ordinal categories help investors make informed decisions.
• Economic Indicators : Some economic measures, like consumer confidence indices, use ordinal scales to gauge public sentiment.

For example, a financial advisor might use an ordinal scale to assess a client's risk tolerance:

• Very Conservative
• Conservative
• Very Aggressive

This ordinal data helps tailor investment strategies to individual client needs.

By leveraging tools like Innerview, researchers and analysts in these fields can streamline the process of collecting and analyzing ordinal data. Innerview's AI-powered analysis can quickly identify patterns and trends across large datasets, saving time and providing deeper insights. This is particularly useful in fields like market research or clinical trials, where large volumes of ordinal data from surveys or patient assessments need to be processed efficiently.

As we've seen, ordinal data is a versatile and powerful tool across various industries. Its ability to capture nuanced information in a structured, ordered format makes it invaluable for decision-making, assessment, and research in diverse fields. Understanding how to effectively collect, analyze, and interpret ordinal data is crucial for professionals looking to make data-driven decisions in today's complex business and research environments.

Analyzing Ordinal Data

Now that we've explored the collection methods for ordinal data, let's dive into the crucial process of analyzing this valuable information. Proper analysis of ordinal data can reveal important insights and trends that might otherwise remain hidden. In this section, we'll explore various techniques for visualizing and statistically analyzing ordinal data, as well as how to interpret the results effectively.

Visual Representation Techniques

When it comes to ordinal data, visual representations can be powerful tools for quickly grasping patterns and distributions. Here are some effective ways to visualize ordinal data:

Bar Charts : These are perhaps the most common and straightforward way to represent ordinal data. Each category is represented by a bar, with the height of the bar indicating the frequency or percentage of responses in that category.

Stacked Bar Charts : Useful for comparing the distribution of responses across different groups or time periods. Each bar represents 100% of responses, with segments showing the proportion in each category.

Diverging Stacked Bar Charts : Particularly effective for Likert scale data. They center the neutral responses and show positive responses to one side and negative to the other, making it easy to see overall sentiment.

Box Plots : While typically used for continuous data, box plots can be adapted for ordinal data to show the distribution and central tendency of responses.

Heat Maps : These can be used to show the frequency of responses across multiple ordinal variables, with color intensity indicating frequency.

When creating these visualizations, it's crucial to maintain the order of the categories as they appear in the original scale. This preserves the inherent order of ordinal data and makes the visualization more intuitive and informative.

Statistical Tests for Ordinal Data

While ordinal data doesn't lend itself to all types of statistical analysis, there are several tests specifically designed for this data type:

Mood's Median Test

This non-parametric test is used to determine whether two or more independent samples have the same median. It's particularly useful when you want to compare the central tendencies of different groups on an ordinal scale.

Mann-Whitney U Test

Also known as the Wilcoxon rank-sum test, this test is used to compare two independent groups. It's often used as an alternative to the t-test when dealing with ordinal data or when the assumptions of the t-test are not met.

Wilcoxon Signed-Rank Test

This test is used for paired samples or repeated measures on a single sample. It's useful for before-and-after studies or when comparing two related samples on an ordinal scale.

Kruskal-Wallis H Test

This test is an extension of the Mann-Whitney U test for three or more independent groups. It's used to determine if there are statistically significant differences between two or more groups of an independent variable on an ordinal dependent variable.

These tests allow researchers to make inferences about populations based on ordinal data samples, providing valuable insights into group differences and relationships between variables.

Interpreting Ordinal Data Results

Interpreting the results of ordinal data analysis requires careful consideration of the nature of the data and the limitations of the analysis methods. Here are some key points to keep in mind:

Central Tendency : For ordinal data, the median is often the most appropriate measure of central tendency. The mode can also be informative, especially for smaller datasets.

Dispersion : Measures like the interquartile range can be used to describe the spread of ordinal data.

Relationships : When examining relationships between ordinal variables, non-parametric correlation coefficients like Spearman's rho or Kendall's tau are appropriate.

Significance vs. Effect Size : While statistical tests can tell you if differences or relationships are significant, it's crucial to also consider the effect size to understand the practical importance of your findings.

Context Matters : Always interpret your results in the context of your research question and the practical significance of your findings.

Limitations : Remember that ordinal data doesn't allow for statements about the magnitude of differences between categories. Avoid treating ordinal data as if it were interval or ratio data.

By leveraging these analysis techniques and interpretation guidelines, researchers can extract meaningful insights from ordinal data across various fields. Tools like Innerview can further enhance this process by providing AI-powered analysis capabilities. For instance, Innerview's automated analysis features can quickly identify patterns across large ordinal datasets from user interviews or surveys, saving researchers significant time and potentially uncovering insights that might be missed in manual analysis.

As we've seen, analyzing ordinal data requires a thoughtful approach, from choosing the right visualization techniques to selecting appropriate statistical tests and carefully interpreting the results. By mastering these skills, researchers and analysts can unlock the full potential of ordinal data, leading to more informed decision-making and deeper insights across a wide range of applications.

Advantages and Limitations of Ordinal Data

Ordinal data, while powerful, comes with its own set of advantages and limitations. Understanding these can help researchers and analysts make informed decisions about when and how to use this data type effectively. Let's explore the benefits, potential pitfalls, and scenarios where ordinal data shines or falls short compared to other data types.

Benefits of Using Ordinal Data in Research

Captures nuanced information.

One of the primary advantages of ordinal data is its ability to capture nuanced information that goes beyond simple yes/no responses. This allows researchers to gain a more detailed understanding of respondents' opinions, preferences, or behaviors. For example, instead of asking if a customer is satisfied, ordinal data lets you gauge the level of satisfaction on a scale, providing richer insights.

Easy for Respondents to Understand and Use

Ordinal scales, such as Likert scales, are generally intuitive for respondents. People find it natural to express their opinions or experiences on a ordered scale, making it easier to collect accurate data. This familiarity can lead to higher response rates and more reliable data.

Flexible Analysis Options

While not as mathematically flexible as interval or ratio data, ordinal data still offers various analysis possibilities. Researchers can use non-parametric tests, calculate medians and modes, and create informative visualizations. This flexibility allows for meaningful interpretation of results across different research contexts.

Efficient Data Collection

Ordinal data often allows for more efficient data collection compared to open-ended questions. It's quicker for respondents to select from a predefined scale, and the structured nature of the data makes it easier to process and analyze, especially when dealing with large sample sizes.

Limitations and Potential Pitfalls

Limited mathematical operations.

One of the main limitations of ordinal data is that it doesn't support all mathematical operations. While you can calculate the median and mode, measures like mean and standard deviation aren't meaningful due to the non-equal intervals between categories. This can restrict the types of statistical analyses you can perform.

Potential for Central Tendency Bias

Respondents may gravitate towards the middle of the scale, especially when they're unsure or want to avoid extreme responses. This central tendency bias can skew results and mask true opinions or behaviors.

Subjectivity in Interpretation

The meaning of ordinal categories can be subjective. What one respondent considers "Agree" might be "Strongly Agree" for another. This subjectivity can introduce inconsistencies in the data and make comparisons across different groups challenging.

Loss of Detailed Information

While ordinal data captures more nuance than nominal data, it still involves some loss of information compared to continuous data. The predefined categories might not perfectly match a respondent's true opinion or experience, leading to a loss of precision.

Difficulty in Measuring Change

It can be challenging to measure the magnitude of change with ordinal data. For example, if a customer's satisfaction moves from "Neutral" to "Satisfied," it's hard to quantify how much their satisfaction has improved.

When to Use Ordinal Data vs. Other Data Types

Use ordinal data when:.

• You need to capture ranked or ordered responses
• The exact difference between categories isn't crucial
• You want to make the survey process easier for respondents
• You're dealing with subjective measures like opinions or attitudes
• You need to collect data from a large sample size efficiently

Consider Other Data Types When:

• Precise measurements are necessary (use interval or ratio data)
• You need to perform advanced statistical analyses that require equal intervals
• The research question demands unordered categorical data (use nominal data)
• You're looking to capture open-ended, qualitative responses (use text data)

By understanding these advantages, limitations, and appropriate use cases, researchers can make informed decisions about when to employ ordinal data in their studies. Tools like Innerview can be particularly helpful when working with ordinal data from user interviews or surveys. Its AI-powered analysis capabilities can quickly identify patterns and trends across large ordinal datasets, helping researchers overcome some of the limitations and extract meaningful insights more efficiently.

Remember, the choice of data type should always be guided by your research objectives, the nature of the information you're trying to capture, and the analysis methods you plan to use. By carefully considering these factors, you can ensure that your use of ordinal data (or any other data type) aligns with your research goals and leads to valuable, actionable insights.

Ordinal Data in the Digital Age

As we step into the digital age, ordinal data has taken on new significance, particularly in the realms of big data analytics and machine learning. Let's explore how this traditional data type is finding new applications and shaping future trends in data analysis.

Role of Ordinal Data in Big Data Analytics

In the era of big data, where vast amounts of information are collected and analyzed daily, ordinal data plays a crucial role in making sense of complex datasets. Here's how:

Simplifying Complex Information

Big data often involves dealing with overwhelming amounts of information. Ordinal data helps simplify this complexity by categorizing information into ordered groups. For example, in analyzing customer behavior across millions of transactions, ordinal scales can be used to categorize purchase frequency (rarely, occasionally, frequently, very frequently), making patterns easier to identify and analyze.

Enhancing Predictive Models

Ordinal data is increasingly being incorporated into predictive analytics models. By including ordinal variables like customer satisfaction levels or product ratings, these models can make more nuanced predictions about future behavior or outcomes.

Improving Data Visualization

In the world of big data, effective visualization is key to understanding trends and patterns. Ordinal data lends itself well to various visualization techniques, such as heat maps or stacked bar charts, which can effectively represent large datasets in an easily digestible format.

Machine Learning Applications with Ordinal Data

Machine learning algorithms are becoming increasingly sophisticated in handling different types of data, including ordinal data. Here are some ways ordinal data is being used in machine learning:

Ordinal Regression

This specialized form of regression analysis is designed specifically for ordinal data. It's used in various applications, from predicting movie ratings to assessing credit risk levels.

Feature Engineering

In many machine learning models, ordinal data serves as valuable input features. For instance, in a model predicting house prices, ordinal variables like neighborhood quality (poor, average, good, excellent) can be crucial predictors.

Sentiment Analysis

Natural Language Processing (NLP) often involves ordinal data, particularly in sentiment analysis. Comments or reviews are frequently categorized on an ordinal scale (very negative, negative, neutral, positive, very positive), allowing for more nuanced analysis of public opinion.

Recommendation Systems

E-commerce and streaming platforms use ordinal data (like user ratings) in their recommendation algorithms to suggest products or content that users are likely to enjoy.

Future Trends in Ordinal Data Usage

As we look to the future, several trends are emerging in how ordinal data is being used and analyzed:

Integration with Other Data Types

There's a growing trend towards integrating ordinal data with other data types for more comprehensive analysis. For example, combining ordinal customer satisfaction data with continuous sales data and categorical product information to gain deeper insights into business performance.

Advanced Visualization Techniques

As data visualization tools become more sophisticated, we can expect to see new ways of representing ordinal data that make it even more intuitive and informative.

Automated Ordinal Scale Creation

Machine learning algorithms are being developed to automatically create optimal ordinal scales from continuous data, potentially uncovering hidden patterns and relationships.

Ethical Considerations

As ordinal data plays an increasingly important role in decision-making algorithms, there's a growing focus on ensuring these categorizations are fair and unbiased, particularly in sensitive areas like healthcare and finance.

Real-time Analysis

With the rise of IoT and edge computing, there's a trend towards real-time analysis of ordinal data. This could lead to more dynamic and responsive systems in areas like customer service or industrial process control.

In this evolving landscape, tools that can efficiently handle and analyze ordinal data are becoming increasingly valuable. Platforms like Innerview, which offer AI-powered analysis capabilities, are particularly well-suited for this new era. By automatically generating insights from user interviews and surveys, often involving ordinal data, Innerview can help researchers and analysts keep pace with the growing volume and complexity of data in the digital age.

As we continue to generate and analyze ever-larger datasets, the importance of ordinal data in providing structured, meaningful insights is only set to grow. Understanding how to effectively collect, analyze, and interpret ordinal data will be a key skill for data scientists and analysts in the years to come.

Discover more insights in: Qualitative Research Methods: A Comprehensive Guide

As we wrap up our comprehensive exploration of ordinal data, it's clear that this versatile data type plays a crucial role across various fields, from market research to healthcare and beyond. Let's recap the key points we've covered and reflect on the importance of understanding and properly utilizing ordinal data in your work or research.

Key Takeaways

• Ordinal data features categories with a natural, meaningful order but non-quantifiable intervals between them.
• It's widely used in surveys, customer satisfaction ratings, and various assessment scales.
• Proper collection methods include well-designed surveys with clear, balanced scales.
• Analysis techniques range from visual representations like bar charts to statistical tests like the Mann-Whitney U Test.
• Ordinal data finds applications in marketing, healthcare, education, and finance, among other fields.
• Understanding ordinal data is crucial for accurate analysis, effective communication, and informed decision-making.
• In the digital age, ordinal data plays a significant role in big data analytics and machine learning applications.

Frequently Asked Questions

What's the main difference between ordinal and interval data? Ordinal data has a clear order but unequal intervals between categories, while interval data has equal intervals between values.

Can I calculate the mean for ordinal data? It's generally not recommended as the intervals between ordinal categories aren't equal. Median and mode are more appropriate measures of central tendency for ordinal data.

What's a Likert scale, and how is it related to ordinal data? A Likert scale is a common tool for collecting ordinal data, typically using a 5 or 7-point scale ranging from "Strongly Disagree" to "Strongly Agree."

How can I visualize ordinal data effectively? Bar charts, stacked bar charts, and heat maps are effective ways to visualize ordinal data.

What statistical tests are appropriate for ordinal data? Non-parametric tests like the Mann-Whitney U Test, Kruskal-Wallis H Test, and Spearman's rank correlation are suitable for ordinal data.

How is ordinal data used in machine learning? Ordinal data is used in various ML applications, including ordinal regression, feature engineering, and sentiment analysis.

Can ordinal data be converted to other data types? While it's possible to convert ordinal data to nominal, it's generally not advisable to convert it to interval or ratio data as it can lead to misinterpretation.

What's the role of ordinal data in customer satisfaction surveys? Ordinal scales are commonly used in customer satisfaction surveys to measure levels of satisfaction, allowing for nuanced feedback beyond simple yes/no responses.

By mastering these concepts and leveraging appropriate tools, you can harness the full potential of ordinal data in your research or business applications. Whether you're gauging customer satisfaction, assessing product quality, or conducting academic research, a solid understanding of ordinal data will enhance the quality and impact of your work.

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Ordinal Data: Definition, Analysis, and Examples

If you work with data in any capacity, you must be familiar with the four major data types: nominal, ordinal, interval, and ratio. This blog will concentrate on ordinal data. We’ll define, explore the characteristics, and provide examples of this data.

Continue reading to learn everything you need to know about this data, its uses, analysis, and the difference between nominal and ordinal data.

LEARN ABOUT: Level of Analysis

Content Index

What is ordinal data?

Uses of ordinal data, characteristics of ordinal data, ordinal data analysis, examples of ordinal data, difference between ordinal data and nominal data, how can questionpro help with managing ordinal data.

Ordinal data is a statistical type of quantitative data in which variables exist in naturally occurring ordered categories. However, it cannot be used to determine the distance between the two categories.

In statistics, a group of ordinal numbers indicates t his data , and a group of this data is represented using an ordinal scale . The main difference between ordinal and nominal data is that ordinal has an order of categories while nominal doesn’t.

Learn more: Nominal vs Ordinal scale

Likert Scale is a popular example of this data . For a question such as: “Please express the importance pricing has for you to purchase a product.”, a Likert Scale will have the following options, which are coded to 1,2,3,4, and 5 (numbers). 1 is lesser than 2, which is lesser than 3, which is lesser than 4, which in turn is lesser than 5.  

Ordinal data is thus a collection of ordinal variables, i.e., if you have variables in a particular order – “low, medium, high,” they can be represented as this kind of data. There are two important factors to consider for this data:

• Multiple terms represent “order,” such as “High, Higher, Highest,” or “Satisfied, Dissatisfied, Extremely Dissatisfied.”
• The difference between variables is not uniform.

Learn more: Types of Measurement Variables

This data is an important type of data in many fields and has a wide range of uses in research and practice. Here, we will discuss some key uses of this data:

Surveys/Questionnaires

It is used for surveys and questionnaires due to its “ordered” nature. Statistical analysis is used to collect responses to categorize the respondents based on their responses. In statistical analysis , distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

The insights of this analysis are used to make inferences and draw conclusions about the respondents in relation to specific variables. It is commonly used for this due to its ease of categorization and collation.

Researchers use this type of data to collect useful information about the subject of their research. Medical researchers, for example, will need to collect data when investigating the side effects of a medication given to 50 patients.

Following the administration of the medication, each patient may be asked to complete a form indicating the extent to which they experience some potential side effects.

Customer service

This data is used by businesses to improve overall customer service. When customers use a service or buy a product from a company, they are often asked to fill out a form about their experience. It will assist businesses in improving their customer service.

Job applications

Employers will occasionally use a Likert scale when collecting information from job applicants during the application process. For example, when an applicant is applying for a position as a social media manager, a Likert scale may be used to determine how familiar an applicant is with Facebook, Twitter, LinkedIn, and so on.

Here are five characteristics of this kind of data such as the following:

• Very Pleasant
• Somewhat Pleasant
• Somewhat Unpleasant
• Very Unpleasant
• Establish a relative rank : In the above-mentioned example, Somewhat pleasant is definitely worse than very pleasant, or very unpleasant is worse than somewhat unpleasant. There is clearly a rank within the options – indicating t his type of data.
• Value of interval is unknown: The variation between pleasant and pleasant may not be the same as the difference between somewhat unpleasant and very unpleasant. You can not include this interval using this data.
• Measure non-numeric traits: All of the answer options in the previous example are non-numeric, and this data can use to capture feelings such as satisfaction, happiness, frequency, and so on.
• Add-on to nominal data: Nominal data is “labeled” data. I t is labeled data in a specific order. In the above mention sample, there is a notable order in the options, which makes it a classic case of this kind of data.
• Ordinal data has a median: The median is the value in the middle of a scale that is not the middle value, and it can calculate with data that has an inherent order.

This  data analysis is a statistical analysis plan for analyzing data with a natural order or ranking. This type of data is frequently gathered through surveys or questionnaires. Let’s explore how you can analyze your ordinal data:

• Easy methods of Ordinal Data analysis

T his data is presented in a tabular format to make analysis easier for the researcher. Mosaic plots also establish the relationship between nominal and ordinal data.

For instance, if an organization intends to analyze the number of employees in each hierarchy to make a systematic hiring process for the upcoming year – they can put this data in an ordered tabular format. HR executives will find this data extremely easy to refer to and analyze for any future updates.   

• Mann-Whitney U test

The Mann-Whitney U test i s used to compare two ordinal data groups. This test allows a researcher to conclude that a variable from one sample is greater or lesser than another variable randomly selected from another sample.

For example, psychological researchers can study behavior patterns to compare two different medicines.

• Kruskal–Wallis H test

To compare more than two groups of this data , Kruskal–Wallis H test should be used – In this test, there is no assumption that the data comes from a particular source. This test concludes whether the median of two or more groups is varied. It will show the difference between more than two groups of this data .

For example, a researcher intends to evaluate the impact of stress at work on the quality of work. In that case, the independent variable will be stress at work which ideally will have three stages: no stress, too much stress, and handleable stress, and the quality of work will vary from poor to excellent.

It is a type of categorical data where the categories have a natural order or ranking. Here are some examples of this type of data:

• 1000 – Freshmen
• 800 – Sophomores
• 750 – Juniors
• 450 – Seniors
• Extremely Happy – 1
• Unhappy – 4
• Extremely Unhappy – 5
• Fire and Fury – 1
• A Higher Loyalty: Truth, Lies, and Leadership – 2
• The Woman in the Window – 3
• The Great Alone – 4
• The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good Life – 5

Data can be classified into various types in statistics based on their characteristics and properties. Ordinal data and nominal data are two examples of such data. Understanding the difference between ordinal and nominal data is important in many statistical analyses because it determines which analysis methods to use. Here, we will learn some key differences between these two types of data:

• Nominal data is data that is categorized without any particular order or ranking. For example, hair color, gender, and types of cars are all examples of nominal data. Ordinal data, on the other hand, is a type of data that has a natural ordering or ranking. It is categorical data that can be ranked or ordered in accordance with a specific attribute or characteristic. Examples of ordinal data are the level of education, the range of income, or the grades.
• You can count, classify and categorize nominal data but cannot rank it. On the other hand, ordinal data can rank or order, but the distinction between each category is not always noticeable or measurable.
• Nominal data use pie charts. On the other hand, Ordinal data use a histogram or bar chart.

QuestionPro is a survey software platform that provides some features and tools to assist researchers and analysts in dealing with various types of data, including ordinal data. QuestionPro can help with this data in a variety of ways. Here are a few examples:

• Designing surveys: You can quickly create questions appropriate for collecting data using QuestionPro’s survey design interface. You can use rating scales, Likert scales, and other types of ordinal response scales to create questions .
• Data collection: There are many ways to gather data with QuestionPro, including online surveys, surveys for mobile devices, surveys for kiosks, and surveys on paper. You can select the best method suited to your specific research needs.
• Data analysis: QuestionPro has a set of powerful tools for analyzing data that are made just for this data. For example, the software can compute the median, mode, range, quartiles, and other descriptive statistics. You can also generate graphs and charts suitable for visualizing this data, such as box plots, histograms, and scatterplots.

LEARN ABOUT: Descriptive Analysis

• Reporting: QuestionPro provides various reporting options to help you communicate your insights after you have collected and analyzed your data. Reports can be generated in various formats, including PDF, Excel, and Word, and they can be customized to include only the data relevant to your research.

LEARN ABOUT: Average Order Value

QuestionPro is a powerful tool for working with ordinal data, and it can help you collect, analyze, and report your data efficiently and effectively.

LEARN MORE         FREE TRIAL

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Nominal, Ordinal, Interval & Ratio Data

By: Derek Jansen (MBA) | Expert Reviewed By Dr. Eunice Rautenbach | November 2020

I If you’re new to the world of quantitative data analysis and statistics, you’ve most likely run into the four horsemen of levels of measurement : nominal, ordinal, interval and ratio . And if you’ve landed here, you’re probably a little confused or uncertain about them.

Don’t stress – in this post, we’ll explain nominal, ordinal, interval and ratio levels of measurement in simple terms , with loads of practical examples .

Overview: Levels of measurement

Here’s what we’ll be covering in this post. Click to skip directly to that section.

• What are levels of measurement in statistics?
• Nominal data
• Ordinal data
• Interval data
• Why does this matter?
• Recap & visual summary

Levels of Measurement 101

When you’re collecting survey data (or, really any kind of quantitative data) for your research project, you’re going to land up with two types of data –  categorical  and/or  numerical . These reflect different levels of measurement.

Categorical data  is data that reflect characteristics or categories (no big surprise there!). For example, categorical data could include variables such as gender, hair colour, ethnicity, coffee preference, etc. In other words, categorical data is essentially a way of assigning numbers to qualitative data (e.g. 1 for male, 2 for female, and so on). 

Numerical data , on the other hand, reflects data that are inherently numbers-based and quantitative in nature. For example, age, height, weight. In other words, these are things that are naturally measured as numbers (i.e. they’re quantitative), as opposed to categorical data (which involves assigning numbers to qualitative characteristics or groups).

Within each of these two main categories, there are two levels of measurement:

• Categorical data – n ominal and o rdinal
• Numerical data – i nterval and r atio

Let’s take look at each of these, along with some practical examples.

Need a helping hand?

What is nominal data?

As we’ve discussed, nominal data is a categorical data type, so it describes qualitative characteristics or groups, with no order or rank between categories. Examples of nominal data include:

• Gender, ethnicity, eye colour, blood type
• Brand of refrigerator/motor vehicle/television owned
• Political candidate preference, shampoo preference, favourite meal

In all of these examples, the data options are categorical , and there’s no ranking or natural order . In other words, they all have the same value – one is not ranked above another. So, you can view nominal data as the most basic level of measurement , reflecting categories with no rank or order involved.

What is ordinal data?

Ordinal data kicks things up a notch. It’s the same as nominal data in that it’s looking at categories, but unlike nominal data, there is also a meaningful order or rank between the options. Here are some examples of ordinal data:

• Income level (e.g. low income, middle income, high income)
• Level of agreement (e.g. strongly disagree, disagree, neutral, agree, strongly agree)
• Political orientation (e.g. far left, left, centre, right, far right)

As you can see in these examples, all the options are still categories, but there is an ordering or ranking difference between the options . You can’t numerically measure the differences between the options (because they are categories, after all), but you can order and/or logically rank them. So, you can view ordinal as a slightly more sophisticated level of measurement than nominal.

What is interval data?

As we discussed earlier, interval data are a numerical data type. In other words, it’s a level of measurement that involves data that’s naturally quantitative (is usually measured in numbers). Specifically, interval data has an order (like ordinal data), plus the spaces between measurement points are equal (unlike ordinal data). 

Sounds a bit fluffy and conceptual? Let’s take a look at some examples of interval data:

• Credit scores (300 – 850)
• GMAT scores (200 – 800)
• The temperature in Fahrenheit

Importantly, in all of these examples of interval data, the data points are numerical , but the zero point is arbitrary . For example, a temperature of zero degrees Fahrenheit doesn’t mean that there is no temperature (or no heat at all) – it just means the temperature is 10 degrees less than 10. Similarly, you cannot achieve a zero credit score or GMAT score. 

In other words, interval data is a level of measurement that’s numerical (and you can measure the distance between points), but that doesn’t have a meaningful zero point – the zero is arbitrary. 

Long story short – interval-type data offers a more sophisticated level of measurement than nominal and ordinal data, but it’s still not perfect. Enter, ratio data…

What is ratio data?

Ratio-type data is the most sophisticated level of measurement. Like interval data, it is ordered/ranked and the numerical distance between points is consistent (and can be measured). But what makes it the king of measurement is that the zero point reflects an absolute zero (unlike interval data’s arbitrary zero point). In other words, a measurement of zero means that there is nothing of that variable.

Here are some examples of ratio data:

• Weight, height, or length
• The temperature in Kelvin (since zero Kelvin means zero heat)
• Length of time/duration (e.g. seconds, minutes, hours)

In all of these examples, you can see that the zero point is absolute . For example, zero seconds quite literally means zero duration. Similarly, zero weight means weightless. It’s not some arbitrary number. This is what makes ratio-type data the most sophisticated level of measurement. 

With ratio data, not only can you meaningfully measure distances between data points (i.e. add and subtract) – you can also meaningfully multiply and divide . For example, 20 minutes is indeed twice as much time as 10 minutes. You couldn’t do that with credit scores (i.e. interval data), as there’s no such thing as a zero credit score. This is why ratio data is king in the land of measurement levels.

Why does it matter?

At this point, you’re probably thinking, “Well that’s some lovely nit-picking nerdery there, Derek – but why does it matter?”. That’s a good question. And there’s a good answer .

The reason it’s important to understand the levels of measurement in your data – nominal, ordinal, interval and ratio – is because they directly impact which statistical techniques you can use in your analysis. Each statistical test only works with certain types of data. Some techniques work with categorical data (i.e. nominal or ordinal data), while others work with numerical data (i.e. interval or ratio data) – and some work with a mix . While statistical software like SPSS or R might “let” you run the test with the wrong type of data, your results will be flawed at best , and meaningless at worst. 

The takeaway – make sure you understand the differences between the various levels of measurement before you decide on your statistical analysis techniques. Even better, think about what type of data you want to collect at the survey design stage (and design your survey accordingly) so that you can run the most sophisticated statistical analyses once you’ve got your data.

Let’s recap.

In this post, we looked at the four levels of measurement – nominal, ordinal, interval and ratio . Here’s a visual summary of each.

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18 Comments

Clear, concise with examples. PLUS in your videos you include other names or terms that could apply to the topic you are reviewing. This is so important! You are fantastic! It has been many, many, many moons ago that these were learned. They are now necessary again as the nursing profession progresses deeper into evidence based practice.

Thanks for the feedback, Diana 🙂

Good explanation.. I came here after giving 5 marks for each question in a quiz n wondering that the data is not continuous and how to analyse it further.. Understood it is ratio and i can use mean/ median accordingly

Glad it helped!

Bloody good! You saved my homework (:

Happy to help 🙂

High quality of education stuff, thank you very much.

great knowledge shared here. I had problem understanding this at the undergraduate school but very clear now. Thanks to GRADCOACH

What type of data would age be? Ratio or interval?

It would be ratio. However, if you are using age ranges (e.g. 18 – 25, 26 – 35, etc.), this wouldn’t be the case.

What is age ranges considered?

What measurement scale is ideal to use when measuring “knowledge” and the acceptable responses are “yes,” “no,” or “not sure”? What kind of analytical test is suitable in this situation?

I watched your youtube tutorial on quantitative analysis and it was really informative.

However, I’m trying to navigate your blog to find the post that discusses the different inferential statistical methods and the data type they support.

Kindly forward this link to my email : [email protected]

Scores from a performance test are ratio data?

It is an excellent discussion about levels of measurement.

Pretty! This was an incredibly wonderful article. Thanks for providing this info.

Thank you so much for this information! The study material was vague.

Thanks for the information very useful with examples

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Home » Ordinal Variable – Definition, Purpose and Examples

Ordinal Variable – Definition, Purpose and Examples

Table of Contents

Ordinal Variable

Definition:

An ordinal variable is a type of categorical variable where the values have a specific order or ranking. The values of an ordinal variable are typically numerical or alphanumeric, but they represent a level of a variable that is not evenly spaced.

In other words, the difference between the values is not necessarily the same, but they have a clear ordering or ranking, such as low to high or small to large.

Types of Ordinal Variables

There are two types of ordinal variables:

Continuous Ordinal Variable

This type of variable has an infinite number of possible values, and the difference between each value is not uniform. Examples of continuous ordinal variables include measures of pain intensity, such as mild, moderate, and severe.

Discrete Ordinal Variable

This type of variable has a finite number of possible values, and the difference between each value is usually uniform. Examples of discrete ordinal variables include letter grades in a course, such as A, B, C, D, and F, or levels of educational attainment, such as high school diploma, associate’s degree, bachelor’s degree, and master’s degree.

Applications of Ordinal Variable

Ordinal variables are widely used in many fields, including:

• Social sciences: In social sciences, ordinal variables are commonly used to represent the level of satisfaction, agreement, or disagreement with a particular concept, such as a political candidate or a product. For example, a survey might ask participants to rate their satisfaction with a product on a scale of 1 to 5, where 1 is very dissatisfied and 5 is very satisfied. The resulting data would be ordinal, with each rating having a clear order.
• Market research : In market research, ordinal variables are often used to measure preferences or attitudes towards a product or service. For example, a survey might ask participants to rank their favorite brands of shoes from most favorite to least favorite. The resulting data would be ordinal, with each brand having a clear rank.
• Education : In education, ordinal variables are commonly used to represent the level of achievement or proficiency of students. For example, a school might use an ordinal grading system, where grades are assigned as A, B, C, D, or F, where each grade represents a higher or lower level of achievement.
• Health care: In health care, ordinal variables are used to represent the severity or progression of a disease. For example, cancer staging systems use ordinal variables to represent the extent to which cancer has spread in a patient’s body.
• Psychology : In psychology, ordinal variables are commonly used to represent the level of agreement or disagreement with a particular statement or concept, such as a personality trait or belief. For example, a questionnaire might ask participants to rank their agreement with statements like “I enjoy socializing with others” on a scale of 1 to 5, where 1 is strongly disagree and 5 is strongly agree. The resulting data would be ordinal, with each rating having a clear order.

Examples of Ordinal Variable

Here are some examples of ordinal variables:

• Educational attainment: This variable represents the level of education achieved by a person, such as high school diploma, associate’s degree, bachelor’s degree, master’s degree, and doctorate degree. The categories have a clear order, but the difference in education level between each category is not uniform.
• Income : Income can be considered an ordinal variable if it is divided into categories such as low, medium, and high. While the difference between the categories is not uniform, they still have a clear order.
• Customer satisfaction: This variable represents the level of satisfaction a customer has with a product or service, usually measured on a scale of 1 to 5 or 1 to 10. The scale has a clear order, but the difference between each rating is not necessarily uniform.
• Pain intensity: Pain intensity can be measured using a scale such as mild, moderate, or severe, where each category has a clear order, but the difference in pain intensity between each category may not be uniform.
• Likert scale: A Likert scale is a type of survey question that measures attitudes or opinions, often using a scale of agreement or disagreement. The categories have a clear order, but the difference between each category is not necessarily uniform.

When to use Ordinal Variable

Ordinal variables are appropriate when the variable being measured can be categorized into a set of ordered levels or ranks. Here are some scenarios when ordinal variables can be used:

• When the data being measured is subjective or cannot be measured using a numerical scale, such as opinions or attitudes.
• When there is a natural order or hierarchy in the data being measured, such as education levels or job ranks.
• When the data being measured has a natural order or progression, such as the stages of a disease or severity of pain.
• When the data being measured is categorical, but the categories are not equally spaced or do not have a natural numerical meaning.

Purpose of Ordinal Variable

The purpose of an ordinal variable is to measure a categorical variable that has a clear order or ranking, but where the difference between the categories may not be equal or may not have a natural numerical meaning.

Ordinal variables allow us to categorize and analyze data that would otherwise be difficult to quantify using numerical variables. They provide a way to rank or order data in a meaningful way that can be used for statistical analysis, such as determining central tendencies or making comparisons between groups.

Ordinal variables are commonly used in research and data analysis in fields such as social sciences, market research, education, health care, and psychology. They allow researchers to measure and analyze data that would otherwise be difficult to quantify and provide valuable insights into attitudes, opinions, preferences, and other important factors.

Characteristics of Ordinal Variable

Here are some key characteristics of ordinal variables:

• Ordered : The categories of an ordinal variable have a clear order or hierarchy. The categories can be arranged in a sequence from the lowest to the highest or from the least severe to the most severe.
• Non-Numeric : Ordinal variables are usually non-numeric, which means that the categories cannot be measured on a numerical scale. For example, the categories of a Likert scale used to measure attitudes or opinions may be “strongly agree,” “agree,” “disagree,” and “strongly disagree,” which do not have a numerical meaning.
• Unequal intervals: The difference between categories of an ordinal variable is not necessarily equal or uniform. For example, the difference between “low,” “medium,” and “high” income levels is not the same as the difference between “low,” “medium,” and “high” levels of education.
• Qualitative : Ordinal variables are a type of qualitative variable, which means they are concerned with qualities or attributes rather than quantities or numbers.
• Limited range: Ordinal variables usually have a limited number of categories or levels. For example, a pain intensity scale may have only three levels: mild, moderate, and severe.

Advantages of Ordinal Variable

Some advantages of using ordinal variables:

• Easy to collect and analyze: Ordinal variables are easy to collect through surveys, questionnaires, or other types of data collection methods. They are also relatively easy to analyze using descriptive statistics and non-parametric tests.
• Allow for ranking and comparison : Ordinal variables allow for ranking and comparison of data that may not have a natural numerical meaning or where the difference between categories is not equal. This can provide valuable insights into attitudes, opinions, preferences, and other important factors.
• Maintain confidentiality: Ordinal variables can be used to measure sensitive or personal data without revealing specific numerical values. For example, a Likert scale used to measure attitudes or opinions may provide valuable insights without revealing individual responses.
• Useful in exploratory research : Ordinal variables can be useful in exploratory research, where the focus is on identifying patterns or relationships in the data rather than making specific predictions or generalizations.
• Flexibility : Ordinal variables can be used in a wide range of research and data analysis applications, including social sciences, market research, education, health care, and psychology.

Limitation of Ordinal Variable

Here are some limitations of using ordinal variables:

• Limited statistical analysis: While ordinal variables are useful for descriptive analysis and non-parametric tests, they are limited in their ability to perform more complex statistical analyses, such as correlation coefficients or ANOVA. This is because the difference between categories may not be uniform or have a natural numerical meaning.
• Arbitrary scale: The scale used to measure ordinal variables is often arbitrary and subject to interpretation. This can lead to inconsistencies in the way data is collected and analyzed, making it difficult to compare results across studies.
• Risk of loss of information: Ordinal variables are less informative than numerical variables, as they only provide information about the ranking or order of data and not the magnitude of the differences between categories. This can lead to a loss of information and limit the insights that can be gained from the data.
• Limited categories : Ordinal variables usually have a limited number of categories or levels, which may not fully capture the complexity of the data being measured. This can lead to oversimplification of the data and limit the accuracy of the analysis.
• Subjectivity: Ordinal variables may be subject to bias or subjectivity in the way they are interpreted or categorized. This can lead to inconsistencies in the data and limit the reliability of the results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is ordinal data? A simple explanation with examples

Last updated

16 April 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

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.

Analyze all your data in one place

Uncover hidden nuggets in all types of qualitative data when you analyze it in Dovetail

• What is ordinal data?

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.

• Ordinal data: examples

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

Income level

Middle level

Upper level

Level of education

Post-secondary

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

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 and other data types

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

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.

• How to collect ordinal data

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.

• Uses of ordinal data

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

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.

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.

• How to analyze ordinal data

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.

• Taking advantage of ordinal data

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.

What are examples of ordinal data variables?

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).

Is age an ordinal variable?

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.

Is gender an example of 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.

Is height nominal or ordinal?

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

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