What is Ordinal Data?

The world around us is inherently filled with order and ranking. We prioritize tasks, judge experiences, and categorize objects - all based on implicit or explicit hierarchies. This intrinsic organization translates into the data we collect and analyze, leading us to a specific type of information: ordinal data.

In this post, we’ll walk through the basics of ordinal data—what it is, and what makes it different. We will explore its key properties, methods of collection and representation, and appropriate statistical approaches for analysis. We will also showcase real-world applications of ordinal data and discuss its limitations and ethical considerations. Let’s dive in!

What is Ordinal Data?

Ordinal data represents categorical variables where the categories have a clear ordinal relationship. This means there is a logical ranking or ordering of the categories, but the distances between them are not necessarily equal. For example, ordinal data could consist of survey responses on a scale from "strongly disagree" to "strongly agree." The categories have a clear ranking, but the difference between "disagree" and "neutral" may not be the same as between "neutral" and "agree." Other common examples of ordinal data include income levels (low, medium, high), education levels (grade school, high school, college, postgraduate), or any other categorical ratings where order matters.

While ordinal data encodes order, it differs from quantitative data on a numeric scale like temperature or income measured in dollars. With ordinal variables, only the order of categories matters, not precise numeric differences. This makes ordinal data useful for conveying rankings and ordered categories in an intuitive way across many different fields.

Understanding Ordinal Data Properties

At the heart of ordinal data lies a fundamental principle: categories are not simply labels, but steps along a ladder. This inherent order distinguishes ordinal data from its close cousin, nominal data, where categories exist in isolation, like guests at a party with no predetermined seating plan. While both share the characteristic of discrete categories, ordinal data takes it a step further by imposing a hierarchy, allowing us to say, for example, that "Strongly Agree" ranks higher than "Agree" on a Likert scale.

However, unlike the evenly spaced steps of a staircase, the intervals between ordinal categories often remain ambiguous. Consider customer satisfaction ratings (Excellent, Good, Fair, Poor): the difference between "Good" and "Excellent" might not be the same as the difference between "Fair" and "Poor." This lack of clearly defined intervals distinguishes ordinal data from interval and ratio data, where units on the scale represent equal distances in terms of the measured attribute. While we can confidently say that 90°C is twice as hot as 45°C (ratio data), comparing "Strongly Agree" to "Agree" on a satisfaction scale is not as straightforward.

Despite this caveat, the presence of order empowers us to perform meaningful analyses. We can calculate the median or mode of an ordinal dataset, revealing which category falls in the middle or represents the most frequent response. We can construct frequency tables and analyze trends in distribution across categories. We can even compare ordinal data across groups using non-parametric statistical tests, such as the Mann-Whitney U test, which are specifically designed for data without fixed intervals.

Real-World Examples of Ordinal Data

Example of Ordinal Data - Likert ScaleImage Source: Wikipedia

  • Likert Scales: These familiar tools measure attitudes or opinions on a graded scale like "Strongly Disagree," "Disagree," "Neutral," "Agree," and "Strongly Agree." The order is clear, but the distance between each step might differ depending on the context.
  • Customer Satisfaction Ratings: From "Excellent" to "Poor," these rankings tell us how happy customers are, but the gap between "Good" and "Excellent" might not be the same as between "Fair" and "Poor."
  • Education Levels: Classifying individuals as "Elementary School," "High School," "College Graduate," or "Postgraduate" establishes an order, but the knowledge and skills gained at each level can vary significantly.
  • Movie Rating Systems: Star ratings (1-5 stars) or thumbs up/down systems provide an ordered ranking of preference, but the difference between a 3-star and a 4-star movie can be subjective and depend on individual taste.
  • Military Ranks: From Private to General, military hierarchy imposes a clear order, but the responsibilities and skills required at each rank differ considerably.

Collecting and Representing Ordinal Data

Having grasped the essence of ordinal data and its unique properties, let's focus on the practicalities of gathering and presenting it accurately. Just like the initial steps of any journey set the course forward, the methods we choose for collecting and representing ordinal data directly shape the insights we ultimately gain.

Methods for Gathering Ordinal Data

There are a range of methods available to tap into the valuable order of ordinal data. Let's delve into some key approaches:

  • Surveys and Questionnaires: These tried-and-true tools are the workhorses of ordinal data collection. Likert scales, ranking tasks, and star ratings are staples in surveys, allowing respondents to express their degree of agreement, preference, or satisfaction. This structured approach ensures clear and consistent data.
  • Interviews and Observations: In qualitative research, semi-structured interviews and structured observations can also yield valuable ordinal data. Imagine interviewers assigning scores to participants' performances based on pre-defined criteria or researchers ranking the severity of observed phenomena. These methods offer rich insights into subjective experiences and nuanced behaviors.
  • Existing Data Sets: Often, readily available data sets already hold valuable ordinal information. Customer feedback databases, educational attainment records, and movie rating platforms can all be rich sources for further analysis. These existing records provide a wealth of data without the need for additional data collection efforts.

Representing Order with Clarity

Building a clear picture of our data's order requires choosing appropriate representation methods. Here are some options:

  • Ordered Categories: This simplest approach lists categories in their natural order, like "Strongly Agree," "Agree," "Neutral," "Disagree," and "Strongly Disagree." While it preserves the ranking, it doesn't capture potential ambiguity in interval size.
  • Numerical Codes: Assigning numerical values to each category (e.g., 5 for "Strongly Agree," 4 for "Agree," etc.) can facilitate calculations, but it risks misleading interpretations of equal intervals between categories.
  • Visual Representations: Bar charts and box plots are powerful tools for depicting the distribution of ordinal data across categories. They highlight trends and central tendencies without assuming equal intervals, offering a clear visual representation of the data's order.

Data Collection Best Practices

To ensure accurate and ethical data collection, focus on these key areas:

  1. Clear Instructions: When collecting data, clearly define the meaning and order of categories to avoid misinterpretations. Use consistent labeling and avoid ambiguous terms. Imagine a confusing survey question where "Strongly Agree" and "Agree" are not clearly differentiated.
  2. Pilot Testing: Before launching a large-scale study, test your data collection methods with a small sample. This helps identify potential biases and ensure clarity in your data. Think of a pilot survey that helps refine the question wording and response options.
  3. Ethical Considerations: Respect participant privacy and anonymity, and be transparent about how the collected data will be used and analyzed. Imagine building trust with your participants by clearly explaining the purpose of your research and how their data will be handled.

Advantages and Disadvantages of Ordinal Data

Ordinal data, with its ability to capture order and preferences, offers valuable insights in various fields. However, like any tool, it has its strengths and limitations. Understanding these trade-offs is crucial for effective data analysis and interpretation.

Advantages of Ordinal Data

  • Unlocks Order and Ranking: Ordinal data shines where simple categories fall short. Unlike nominal data, it reveals not just "what" but also "how much" or "in what order," providing information about preferences, rankings, and degrees of something. Imagine a customer satisfaction survey with ratings from "Extremely Unsatisfied" to "Highly Satisfied." This data tells us not just whether customers are happy or not, but also how strongly they feel.
  • Easy to Collect and Understand: Gathering ordinal data often involves straightforward methods like surveys, questionnaires, and rating scales. These familiar tools make it readily accessible for researchers and users across various disciplines.
  • Versatile Applications: From gauging student performance in education to monitoring patient well-being in healthcare, ordinal data finds diverse applications. Its ability to capture rankings and preferences proves valuable for decision-making in numerous sectors.

Disadvantages of Ordinal Data

  • Limited Information Compared to Interval and Ratio Data: While ordinal data reveals order, it lacks the precise measurements and calculations possible with interval and ratio data. Imagine using Likert scales to measure income levels. While we can understand relative comparisons (e.g., higher or lower), we cannot calculate exact differences between income brackets.
  • Potential for Bias in Data Collection: The design and wording of questions can influence responses and introduce bias in ordinal data. Careful crafting of surveys and questionnaires is crucial to collect accurate and meaningful information.
  • Limitations in Statistical Analysis: Not all statistical methods are suitable for ordinal data. Averaging rankings, for example, can be misleading due to potentially unequal intervals between categories. Therefore, specialized non-parametric tests are often required for accurate analysis.

Analyzing and Interpreting Ordinal Data

When working with ordinal variables, we need analysis techniques that respect the inherent order without assuming consistent intervals between categories. Let's explore some of the main methods for analyzing ordinal data:

Measures of Central Tendency

The mode and median are useful measures of central tendency for ordinal variables. The mode shows the most frequently occurring category, while the median identifies the middle category that divides the data into equal halves. For example, if "Agree" was the most common response on a 5-point satisfaction scale, it would be the mode. The median could be "Neutral" if that category split the data into 50% above and below it. Using the mean is typically not appropriate, since it assumes consistent intervals.

Exploring the Distribution

Frequency tables tally up the occurrences of each ordinal category, providing a clear picture of the distribution of responses. For example, a frequency table could show 50% of responses as "Agree", 30% as "Neutral", and 20% as "Disagree".

Percentiles further break down the distribution by dividing the data into 100 equal parts. This helps locate where values fall relative to the overall sample. For instance, the 80th percentile means 80% of responses are at or below that data point.

Non-Parametric Statistical Tests

Since ordinal variables do not meet normality and equal variance assumptions, parametric tests like ANOVA and t-tests cannot be used. Instead, non-parametric tests are employed as they do not rely on these assumptions. Two common non-parametric tests used with ordinal data are the Mann-Whitney U test to compare two groups, and the Kruskal-Wallis H test to compare three or more groups. These assess if rank distributions differ significantly between groups.

Ordinal Regression

Regression techniques like ordinal logistic regression can model the relationship between ordinal predictor variables and a categorical outcome variable. This estimates the probability of ranked outcomes based on the predictor values. Ordinal regression suits analysis of ordinal scales with more than two categories, as binary logistic regression is better for dichotomous outcomes.


Visualizing ordinal data maintains the order and ranking of categories. Options like ordered bar charts, column charts, and dot plots are appropriate. Line graphs imply a continuous numeric scale and should be avoided. Plots displaying distributional statistics like quartiles or percentiles are also useful. Visuals make patterns and trends in the data more interpretable.

Applications of Ordinal Data

Ordinal data's unique ability to capture order makes it a valuable tool across various fields. Let's explore how its insights illuminate research and decision-making in real-world applications:

Market Research

Understanding customer preferences and satisfaction is crucial for businesses. Ordinal data from surveys and ratings (e.g., product satisfaction, brand loyalty) helps companies gauge market sentiment, prioritize product development, and refine marketing strategies.

Social Sciences

Ordinal data plays a vital role in understanding human behavior and societal trends. Studies on education levels, income brackets, or political leanings utilize ordinal data to analyze social inequalities, track socio-economic changes, and inform policy decisions.

Psychology and Mental Health

Ordinal scales measuring pain intensity, depression symptoms, or quality of life are integral to psychological research and clinical assessments. Analyzing such data allows researchers to evaluate treatment effectiveness, track disease progression, and personalize interventions for patients.

Apart from these examples, ordinal data finds applications in many other diverse fields. In education, it assesses student performance, tracks learning outcomes, and compares teaching methods. Healthcare leverages it to measure patient satisfaction, monitor recovery progress, and prioritize resource allocation. Even in consumer research, ordinal data analyzes user experience, clarifies product engagement, and guides usability improvements. This diverse range showcases the immense potential of ordinal data to inform impactful decisions across numerous sectors.

In summary, ordinal data encodes important rankings between categories, but lacks consistent quantifiable units. This presents both opportunities in the form of nonparametric analysis options, and limitations in terms of statistical power. While ordinal variables do not provide the precision of true numeric data, their ranking ability enables insightful modeling of subjective judgements and preferences. Approached diligently, ordinal data can reveal key patterns to inform decision making across many critical domains.

Standardize Ordinal Data with RudderStack

Modern CDPs such as RudderStack makes it possible to collect ordinal data and use it effectively to understand and improve your marketing efforts.

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