What is Ordinal Data: Definition, Analysis and Examples

What is ordinal data? This question baffles researchers and data enthusiasts equally.

Ordinal data is an interesting form of qualitative information that fills the space between categorical and numerical data. It is often overlooked in surveys and questionnaires, yet it offers important information on preferences, rankings, and attitudes.

Picture that terrible, ordinal-less world. We can’t figure out customer satisfaction levels or educational achievements, either. Fortunately, ordinal data exists and is common, though you may not immediately realize it. It is ubiquitous, from Likert scales to military ranks.

Did you know that 78% of data scientists consider ordinal data key to exploratory analysis? Well, the fact that it is versatile and super sensitive for picking up so much additional nuance in information makes sense. Ordinal data allows us to rank categories without assuming equal intervals between them. This unique characteristic makes it invaluable in fields ranging from psychology to market research.

But what is ordinal data capable of? It can reveal patterns in customer preferences, helping businesses make informed decisions. In education, it’s used to grade students and evaluate performance. Even in healthcare, ordinal data is crucial in assessing pain levels and treatment outcomes.

Understanding ordinal data is key to unlocking its potential. It’s not as simple as nominal data nor as precise as interval data. It occupies a sweet spot, offering rich insights without the need for complex mathematical operations.

Let’s demystify ordinal data – its characteristics, applications, and the powerful stories it can tell.

Table of Contents:

1. What is Ordinal Data?
2. Key Characteristics of Ordinal Data
3. Why is Ordinal Data Important?
4. Explain Examples of Ordinal Data
5. Advantages and Disadvantages of Using Ordinal Survey Questions
6. What are Ordinal Data Analysis Methods?
7. How is Ordinal Data Collected and What is it Used for?
8. How to Analyze Ordinal Data?
9. Applications of Ordinal Data
10. Ordinal Data: FAQs
11. Wrap Up

First…

What is Ordinal Data?

Definition: Ordinal data is categorical data with a clear order or ranking among its categories. Ordinal data has a meaningful sequence, unlike nominal data, but the intervals between categories are not necessarily equal.

For example, customer satisfaction levels (e.g., “satisfied,” “neutral,” “dissatisfied”) are ordinal because they follow a logical order. However, the difference between “satisfied” and “neutral” may not be the same as between “neutral” and “dissatisfied.”

Ordinal data is commonly used in surveys, rankings, and any context where items can be compared but not measured precisely.

Key Characteristics of Ordinal Data

Ordinal data is categorical data with a meaningful order or ranking among its categories. Unlike nominal data, the order matters, but the intervals between values are not necessarily uniform or meaningful.

It often represents levels of agreement, satisfaction, or other ranked attributes. Examples include Likert scale responses (e.g., strongly agree to strongly disagree) or socioeconomic status levels (e.g., low, medium, high).

While it can be sorted, arithmetic operations like addition or subtraction are not appropriate for ordinal data.

Why is Ordinal Data Important?

Ordinal data is the backbone of many decisions, even if we don’t always realize it. It’s the secret sauce behind those ranked lists and scales that help us understand the world around us. Here’s why it’s so important:

• Rank order insights: It helps us see the order of preferences or priorities, like which product features customers value most.
• Decision-making: It provides a structured way to compare options, making it easier to choose between alternatives.
• Simplified analysis: It simplifies complex data into easy-to-understand rankings, streamlining analysis and interpretation.
• Behavioral understanding: Ordinal data offers a glimpse into how people think and feel by ranking their attitudes or behaviors. It provides deeper insights into human behavior.

Explain Examples of Ordinal Data

Ordinal data represents categories with a meaningful order but without a consistent scale between them.

Examples of ordinal data include customer satisfaction ratings, such as “very satisfied,” “satisfied,” and “dissatisfied,” which show an order of preference but do not quantify the difference between levels.

Education levels like “high school,” “bachelor’s,” and “master’s” also fall into this category, as they indicate progression but lack a uniform gap.

Pain scales from “no pain” to “severe pain” and product reviews using star ratings are further examples.

These types of data help in understanding the relative positioning of elements but not the precise difference between them.

Advantages and Disadvantages of Ordinal Survey Questions

Ordinal survey questions are a typical kind of inquiry design utilized in surveys to quantify respondents’ mentalities, discernments, or inclinations.

These inquiries present a bunch of requested reaction choices, permitting members to rank their responses in light of inclination or understanding.

A normal model is the Likert scale (e.g., “Unequivocally Conflict” to “Firmly Concur”). While ordinal questions give important experiences, they likewise accompany impediments.

Advantages of Ordinal Survey Questions

• Easy to Understand and Use

Respondents can rapidly get a handle on the importance of ordinal scales, making overviews simpler to finish.

• Simplifies Data Collection

Since responses are ranked, it simplifies analysis compared to open-ended questions.

• Captures Perceptions and Attitudes

Useful for measuring subjective variables such as satisfaction, agreement, or importance.

• Provides a Clear Order of Preference

Enables researchers to understand trends and patterns in respondent opinions.

• Allows for Statistical Analysis

Middle, mode and non-parametric measurable tests (like the Mann-Whitney U test) can be utilized for examination.

Disadvantages of Ordinal Survey Questions

• Limited Precision

Differences between response options are not necessarily equal, making it difficult to quantify exact differences.

• Cannot Measure the True Magnitude of Differences

While ordinal data shows order, it does not indicate the intensity of differences between choices.

• Subject to Interpretation Bias

Respondents may interpret scale points differently, leading to inconsistencies.

• Restricts Advanced Statistical Analysis

Many statistical tests require interval or ratio data, limiting how ordinal data can be analyzed.

• Forces Responses into Predefined Categories

May not capture the full complexity of a respondent’s opinion.

What are Ordinal Data Analysis Methods?

Analyzing data is all about understanding rankings and order without getting lost in exact numbers. Several methods exist to make sense of this data type, each offering unique insights. Here are some key approaches:

Descriptive Statistics:

• Frequency Distribution: Shows how often each rank or category occurs.
• Mode: Identifies the most common category or rank.
• Median: Highlights the middle rank, giving a sense of central tendency.

Visualizations:

• Bar Charts: Bar Charts visually compare the frequency of each rank.
• Box Plots: Box Plots display the spread and central value of ordinal data.

Non-Parametric Tests:

• Chi-Square Test: Assesses relationships between categorical variables.
• Mann-Whitney U Test: Compares differences between two independent groups.
• Kruskal-Wallis Test: Evaluates differences among three or more groups.

Ordinal Regression:

• Ordinal Logistic Regression: Models relationships between ordinal outcomes and predictor variables.

Rank Correlation:

• Spearman’s Rank Correlation: Measures the strength and direction of the association between two ranked variables.

How is Ordinal Data Collected and What is it Used for?

Ordinal data is collected through surveys, questionnaires, or assessments where respondents rank or rate options in a specific order. Common collection methods include Likert scale questions, rating scales, and preference rankings.

This type of data captures relative positioning but not the precise difference between ranks. For instance, a consumer loyalty study could utilize a scale from “exceptionally disappointed” to “extremely fulfilled.”

Ordinal information is utilized to quantify abstract evaluations, inclinations, and discernments. It is valuable in social sciences, marketing, and healthcare to analyze trends, compare groups, or track changes over time.

Though it indicates order, it doesn’t quantify the exact distance between points, making it crucial for ranking and prioritization purposes.

How to Analyze Ordinal Data?

Data analysis: where numbers go to a party, but analysts get the hangover. Ordinal data? It’s more like ordeal data!

Enter the hero: data visualization. It’s the aspirin for your analytical headache.

But wait. Excel has stage fright at the onset of graphs for ordinal data. It’s like bringing a spoon to a knife fight.

Frustrating, right?

Fear not, data warriors! ChartExpo crashes the party, armed with visual superpowers, including stunning statistical graphs. It’s the Robin to Excel’s Batman, filling in the visualization gaps.

Let’s give your data the spotlight it deserves!

Let’s learn how to install ChartExpo in Excel.

1. Open your Excel application.
2. Open the worksheet and click the “Insert” menu.
3. You’ll see the “My Apps” option.
4. In the Office Add-ins window, click “Store” and search for ChartExpo on my Apps Store.
5. Click the “Add” button to install ChartExpo in your Excel.

ChartExpo charts are available both in Google Sheets and Microsoft Excel. Please use the following CTAs to install the tool of your choice and create beautiful visualizations with a few clicks in your favorite tool.

Example of Ordinal Data

Let’s analyze the ordinal data sample below using ChartExpo.

Questions	Scales	Responses
How well does our product meet your needs?	1	130
How well does our product meet your needs?	2	136
How well does our product meet your needs?	3	128
How well does our product meet your needs?	4	968
How well does our product meet your needs?	5	638
How satisfied are you with our team in resolving your issue?	1	186
How satisfied are you with our team in resolving your issue?	2	278
How satisfied are you with our team in resolving your issue?	3	483
How satisfied are you with our team in resolving your issue?	4	539
How satisfied are you with our team in resolving your issue?	5	514
How satisfied are you with your in-store experience?	1	148
How satisfied are you with your in-store experience?	2	130
How satisfied are you with your in-store experience?	3	193
How satisfied are you with your in-store experience?	4	665
How satisfied are you with your in-store experience?	5	864
How satisfied are you with product packing?	1	145
How satisfied are you with product packing?	2	240
How satisfied are you with product packing?	3	104
How satisfied are you with product packing?	4	667
How satisfied are you with product packing?	5	844

• To get started with ChartExpo, install ChartExpo in Excel.
• Now Click on My Apps from the INSERT menu.

• Choose ChartExpo from My Apps, then click Insert.

• Once it loads, choose the “Likert Scale Chart” from the charts list.

• Click the “Create Chart From Selection” button after selecting the data from the sheet, as shown.

• ChartExpo will generate the visualization below for you.

• If you want to have the chart’s title, click Edit Chart, as shown in the above image.
• Click the pencil icon next to Chart Header to change the title.
• It will open the properties dialog. Under the Text section, you can add a heading in Line 1 and enable Show.
• Give the appropriate title of your chart and click the Apply button.

• You can change the colors of Axis values as follows:

• You can change the colors of Bars as follows:

• You can change the font size of Questions as follows:

• You can disable the Grid lines as follows:

• You can change the colors of inner labels as follows:

• You can change the scale number into words as follows: 1= Very dissatisfied, 2=Dissatisfied, 3=Neutral, 4=Satisfied, 5=Very satisfied

• Click the “Save Changes” button to persist the changes.

• Your final chart will appear below.

Insights

• Highest Satisfaction: Product packing and in-store experience received the most “Very Satisfied” (5) ratings.
• Good Scores: Product needs and issue resolution also scored well but mostly as “Satisfied” (4).
• Minimal Neutral/Dissatisfied Responses: Few neutral or dissatisfied responses were noted across all categories.

Applications of Ordinal Data

Ordinal data finds applications in various fields where ranking or ordered categories are essential. In education, it’s used to grade students (e.g., A, B, C). In customer satisfaction surveys, ordinal data helps gauge service quality (e.g., poor to excellent). It’s also used in healthcare for pain scales, measuring patient discomfort levels. Additionally, ordinal data aids in market research to rank product preferences or brand loyalty.

FAQs

How is ordinal data different from nominal data?

Ordinal data has a meaningful order or ranking, like customer satisfaction levels. Nominal data, however, are categorized without any inherent order, such as the types of fruit. Ordinal data shows relative position, while nominal data does not.

What are the limitations of ordinal data?

Ordinal data only shows order, not the exact difference between categories. It can’t measure the magnitude of differences or perform arithmetic operations. This limits its use in precise statistical analysis and can lead to less detailed insights.

How do you interpret ordinal data in a visual graph?

Ordinal data is shown with ordered categories in a visual graph. Use bar charts or line graphs to display the ranking. Ensure the order is clear, but remember that the distances between categories are not quantified.

Wrap Up

Ordinal data is categorical data with a meaningful order. It ranks items or responses sequentially but doesn’t measure their exact differences. This makes it different from nominal data, where there’s no inherent order. It’s also different from interval or ratio data, which involve precise measurements.

Ordinal data is widely used in surveys and questionnaires. It helps capture preferences, levels of satisfaction, or grades in a structured way. This makes it useful in many fields, from market research to education.

The key advantage of ordinal data is its simplicity. It allows easy interpretation and straightforward analysis. You can quickly understand how different categories or responses rank relative to each other.

However, ordinal data has its limitations. It lacks precision, and the intervals between ranks are not equal. This restricts the types of statistical methods you can use. Despite this, it’s still a flexible tool for gathering insights.

In essence, ordinal data provides a valuable way to understand order and rank without needing exact measurements. It’s a practical choice for many types of research, offering clarity and simplicity in analysis. Understanding its strengths and limitations is crucial for effective use.