Nominal vs. Ordinal Data: What’s The Difference?

When you think about data, what comes to mind? Most people immediately think about numerical data — numbers that can be measured and quantified — but that's only part of it.

The term data refers to any collection of information or measurements that can be analyzed to provide insights or make decisions. Everything from our age, height, and weight to our favorite colors, hobbies, and daily routines can be measured and classified into different types of data.

Once we understand how to classify this data, we can make informed decisions, spot trends, and better understand various aspects of life and behavior.

Of the many types of data, two common types are nominal data and ordinal data, which group information into categories based on qualitative attributes. (This is known as “categorical data”.) Each serves a different purpose, and it's essential to know the differences — because they affect the statistical methods used and the accuracy of conclusions. In brief:

• Nominal data categorizes without order, allowing only for qualitative analysis

• Ordinal data introduces a meaningful ranking, bridging into quantitative analysis.

Let's dive a little deeper into the distinctions between nominal and ordinal data.

What is Nominal Data?

Nominal data categorizes items or variables into distinct groups without any inherent order or ranking. These categories are simply labels or names without any quantitative value or hierarchy.

Nominal data is often represented by words or symbols to distinguish between categories. These categories are mutually exclusive, meaning an individual can only fit into one category at a time.

Nominal data is generally non-numerical and cannot be used in calculations.

Key characteristics

• Categories are mutually exclusive and cannot overlap

• No intrinsic order or ranking among categories

• Mathematical operations like addition or multiplication are meaningless

• Mode is the only way to find the most common category

Examples of nominal data

• Gender: male, female, other

• Marital status: single, married, divorced, widowed, other

• Favorite color: red, blue, purple, yellow, etc.

• Types of pets: cat, dog, fish, bird, etc.

• Blood type: A, B, AB, O

Nominal data in real-world scenarios

• Marketing: Segmenting customers based on favorite products.

• Healthcare: Categorizing patients by blood type.

• Education: Classifying students by extracurricular activities.

Appropriate measures and techniques

Because nominal data is categorical, the range of applicable statistical measures is limited. The mode is typically used to identify the most frequent category. Frequency distributions can summarize how often each category occurs.

Visualization techniques include:

• Bar Charts are ideal for displaying the frequency of categories within nominal data. Each bar represents a category, and its height corresponds to the category’s frequency.

• Pie Charts are used to show the proportion of each category within the whole dataset. Each slice of the pie represents a category, and the size of the slice is proportional to the frequency of that category.

What’s Ordinal Data?

Ordinal data categorizes items or variables into distinct groups with a meaningful order or ranking. Although the categories have a natural order, the differences between them are not necessarily equal or quantifiable.

Ordinal data is often represented by numbers or words to indicate the rank of each category.

Ordinal data: key characteristics

• Categories have a clear order or ranking

• Differences between categories may not be equal or quantifiable

• Mathematical operations like addition and multiplication are not meaningful, but comparisons like greater than or less than are possible

• Median and percentiles are good for finding the middle and comparing rankings

Examples

• Education level (high school, bachelor's, master's, doctorate)

• Customer satisfaction ratings (very dissatisfied, dissatisfied, neutral, satisfied, very satisfied)

• Income levels (low, middle, high)

• Likert scale responses (strongly disagree, disagree, neutral, agree, strongly agree)

• Movie ratings (One star, two stars, three stars, four stars, five stars)

Ordinal data in real-world scenarios

• Customer Service: Analyzing satisfaction ratings to improve service.

• Human Resources: Ranking employees’ performance levels.

• Market Research: Evaluating consumer preferences on a scale.

Appropriate measures & techniques

Due to the ordered nature of ordinal data, certain statistical techniques and measures are particularly relevant. The median identifies the central point of the dataset, providing a measure of central tendency. Percentiles divide the dataset into 100 equal parts, allowing you to compare positional rankings.

Visualization techniques include:

• Bar Charts: Similar to nominal data, bar charts are useful for ordinal data to illustrate the frequency of each category. The bars are ordered according to the inherent ranking of the categories.

• Dot Plots: These can show the distribution of individual data points within categories, highlighting the spread and clustering within the ranked groups.

• Ordered Bar Charts: An enhancement of the standard bar chart, ordered bar charts display categories in sequential order, providing a visual representation of the ranking.

Key differences: Nominal vs. ordinal data

Despite their similarities in being forms of categorical data, nominal and ordinal data differ fundamentally in how they are treated and analyzed statistically. Here are some of the key differences between nominal and ordinal data:

Order and ranking

• Nominal Data: Lacks any intrinsic order or ranking among the categories (e.g., types of pets). Each category stands alone without any implied positioning relative to others.

• Ordinal Data: Possesses a clear order or ranking among categories (e.g., education levels), although the intervals between the categories are not necessarily equal or quantifiable.

Mathematical operations

• Nominal Data: Mathematical operations such as addition, subtraction, multiplication, and division are meaningless for nominal data. The primary focus is on identifying and counting the categories.

• Ordinal Data: While addition and multiplication remain inappropriate, comparisons such as greater than, less than, or equal are possible. The ordinal nature allows for the determination of the median and percentiles.

Finding the average

• Nominal Data: The mode, which identifies the most frequently occurring category, is the only measure of central tendency applicable to nominal data.

• Ordinal Data: Both the median and the mode are suitable measures of central tendency. The median provides insight into the central value or position of the data when ordered.

Visualization techniques

• Nominal Data: Best represented using bar charts and pie charts that display the frequency or proportion of each category.

• Ordinal Data: While bar charts can be used, they should respect the inherent order of the categories. Ordered bar charts and dot plots are also effective in illustrating the ranked nature of the data.

Data analysis

• Nominal Data: Focuses on frequencies and proportions, often summarized through tables and basic charts. Analysis involves counting and comparing the sizes of different categories.

• Ordinal Data: Supports more sophisticated analytical techniques that consider the order of categories, such as median splits, percentile ranks, and non-parametric statistical tests like the Mann-Whitney U test.

Understanding these distinctions is crucial for selecting the appropriate statistical methods and visualizations when working with categorical data. Applying the correct techniques ensures that the analysis accurately reflects the nature of the data and yields meaningful conclusions.

Importance of Correct Classification

Correct classification of data is crucial in data analysis as it directly affects the selection of statistical methods and the interpretation of results. Misclassifying data can lead to incorrect assumptions and flawed conclusions.

Consider a customer satisfaction survey where respondents rate their experience on a scale from 1 (very dissatisfied) to 5 (very satisfied). This rating scale represents ordinal data. If treated as nominal data, the order or ranking is ignored, leading to inaccurate representations and missed trends.

For example, calculating the mode instead of the median would not capture the order of satisfaction levels, potentially misinforming business strategies.

Treating ordinal data as nominal can result in the loss of valuable information regarding the order of categories, while treating nominal data as ordinal can introduce biases by implying a nonexistent order.

Summarizing nominal vs. ordinal data

Understanding the distinctions between nominal and ordinal data is essential for accurate data analysis.

Recognizing these differences ensures the correct application of statistical techniques, preserving the integrity of your analysis. However, misclassifying these data types can result in invalid insights and flawed decisions. By correctly identifying and analyzing nominal and ordinal data, you can gain more accurate and meaningful conclusions from your datasets.