What is Ordinal Data and How to Handle It in Surveys?

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. When to Use Ordinal Data?
3. Why is Ordinal Data Important?
4. What are Ordinal Data Analysis Methods?
5. What are the Advantages and Disadvantages of Ordinal Data?
6. How to Analyze Ordinal Data?
7. 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.

When to Use Ordinal Data?

Ordinal data is your go-to choice when you need to rank or order things, but don’t require precise measurements. It’s like knowing who came in first, second, and third in a race without needing their exact times. Here’s when to use it:

• Ranking preferences: When you want to understand what people like most or least, like ranking favorite foods or movies.
• Levels of achievement: Perfect for grading or classifying levels, such as educational attainment or job performance.
• Surveys and questionnaires: Great for responses that involve scales, like “strongly agree” to “strongly disagree.”
• Behavioral research: Useful in studies that rank behaviors or attitudes, like levels of aggression or satisfaction.

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.

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.

What are the Advantages and Disadvantages of Ordinal Data?

Ordinal data has perks and challenges, making it a popular yet sometimes tricky tool in data analysis. Let’s explore both sides:

Advantages of Ordinal Data:

• Ease of interpretation: Ordinal data is easy to understand and communicate – it involves straightforward rankings or orderings.
• Useful for surveys and questionnaires: It’s ideal for capturing respondents’ preferences or opinions in a simple, structured way.
• Simplifies analysis: Ordinal data simplifies complex data into manageable insights by focusing on the order rather than exact values.
• Flexibility: Ordinal data can be applied across various fields, from customer satisfaction surveys to academic research.

Disadvantages of Ordinal Data:

• Lack of precision: It doesn’t capture the exact differences between categories, which can limit the depth of analysis.
• Limited statistical methods: Fewer statistical techniques are available for ordinal data compared to interval or ratio data.
• Subjectivity: Interpreting ranks can be subjective, as the meaning of each category might vary among respondents.
• Reduced detail: Ordinal data may oversimplify complex information, potentially leaving out important nuances.

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. 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 as 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.

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.