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The Ultimate Guide to Understanding the Ordinal Scale in Research

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In survey and market research, data collection is an integral step that leads you to make effective business decisions. To make informed decisions, researchers leverage various measurement scales to quantify the data. One such scale is the ordinal scale. 

The ordinal scale is one of the ways marketers collect qualitative data by associating numerical values. It allows you to assign order or rank to various response options in your survey. The scale captures the inherent order, which helps you gain a deeper understanding of the survey result and facilitates comprehensive analysis. 

This blog will dive deep into everything about the Ordinal Scale, exploring its definition, how to measure ordinal data, some types, and examples of ordinal scale questions.   

What is the Ordinal Scale?

Ordinal Scale is listed 2nd in the four ‘Levels of Measurement’, as described by S.S. Stevens. The Ordinal scale includes statistical data type where variables are in order or rank but without a degree of difference between categories.

The ordinal scale contains qualitative data; ‘ordinal’ meaning ‘order’. It places variables in order/rank, only permitting to measure the value as higher or lower in scale. The scale cannot generate a precise comparison between the two categories.

Let’s take the horse race as an example of ordinal scale. in a horse race, we only see the ranking of the horses that won as 1st, 2nd, and 3rd. The ranks don’t tell us by how much distance did the first horse win or the third horse lose.

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Characteristics of Ordinal scale

  1. You can use an ordinal scale for research and survey purposes to understand the higher or lower value of a data set. The scale identifies the magnitude of the variables.
  2.  It does not explain the distance between the variables. The ordinal scale cannot answer “how much” different the two categories are.
  3. Like a Likert scale, the ordinal scale can measure frequency, importance, satisfaction, likelihood, quality, and experience, etc.
  4. The measures in ordinal scale do not have absolute value hence the real difference between adjacent values may not have the same meaning. For example, the values in the age scale “less than 20” and “20-50” do not have the same meaning as “50-80” and “over 80”.

How to measure data collected using the Ordinal Scale?

Data you obtain using an ordinal scale can be evaluated using descriptive & inferential statistical analysis. So let’s explore the two types of statistical approaches to analyzing ordinal data. 

Descriptive Statistics

The frequency distribution table can inform the number of times each response was selected.

Let’s take the following ordinal scale example, you ask 30 respondents. 

Q. I take home-cooked meals to the office. 







Frequency Distribution Table

So, which of the following data is measured on an ordinal scale using descriptive statistics?

Agreement level












**You can visualize the data in a bar graph. It is important to remember that the categories used in the ordinal scale should be in proper order when displaying data. 

To find the Central Tendency, you can calculate the mode or the median.

The mode can be found in almost all ordinal scale data. But, the median can be found in some cases. 

Based on the above dataset, the mode is “Always.” Mode is the value that appears most frequently in your dataset. 

The value of the median is found in two ways for odd & even-numbered datasets. 

Odd-numbered dataset: The median is the middle value of the dataset. 

Even-numbered dataset: The median is the mean of the two values in the middle.In this example, the median would be the value at the 15th, and 16th positions,i.e., “Often”.

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Inferential Statistics

Non-parametric tests are used in the case of inferential statistics. The following are non-parametric tests you can use to analyze ordinal data. 

Non-parametric test


Samples or Variables

Mood’s median test

Compare the medians

2 or more samples

Mann-Whitney U test

Compare sum of rankings of the scores

2 independent samples

Wilcoxon matched-pairs signed-rank test

Compare magnitude & direction of difference between the distribution of scores

2 dependent samples

Kruskal- Wallis H test

Compare the mean ranking of scores

3 or more samples

Spearmen’s rho, or rank correlation coefficient

Correlate 2 variables

2 ordinal variables

Types of Ordinal Scales to use

Here we have five types of ordinal scales you can use to gather customer data. A robust online survey tool should provide you with the following types enabling you to write survey questions as per research requirements. 

Here we will look into five examples of ordinal scale question types. 

  • Familiarity
  • Agreement
  • Frequency
  • Satisfaction
  • Likelihood 

Let’s see how each of these types can help you in your research. 


The familiarity ordinal scale can help you assess the level of knowledge your respondents have about the topic. 

Very Familiar

Quite Familiar

Moderately Familiar

Somewhat Familiar

Not at all Familiar

A brand can use this example of ordinal scale type to test how familiar their audiences are with the brand’s product. 


This scale can help determine how much your respondents agree/disagree with your statement.

Strongly Agree




Strongly Disagree 

A company can evaluate employee opinion on work-life balance. The degree of agreement or disagreement can help identify how the company can improve employee perception. 

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This ordinal scale can inform you how often an activity is performed to help you evaluate the behavior pattern. 






An ordinal scale example of this type is how a coffee brand can survey prospective customers about how often they drink coffee in a week. The brand can use the result to segment customers based on their coffee intake and develop strategies suitable for each segment. 


The best way to understand how satisfied your customers, employees, and prospects are with your services and products. 

Very Satisfied




Very Dissatisfied 

Gather customer satisfaction at every touchpoint in their journey using this scale to determine which touchpoint needs improvement and where you are excelling. 


This scale helps you understand how likely the respondents will perform the suggested activity. 





Not at all

A company can use this to identify which respondents are most likely to recommend your product to friends and family members. 

Now that we have learned about various types of ordinal scales in research let’s look at some of its examples. 

Examples of ordinal scale

Here we will look into some ordinal scale examples to demonstrate how you can use the scale question across various industries to gather valuable insights. 

1. Market research:

  • How would you rank the user-friendliness of our product on a scale of 1-5?
  • How likely are you to purchase from our company in the next six months?

2. Hospitality research:

  • Help us understand which amenities are important during your stay. Please rank the following: a) Complimentary breakfast, b) Swimming pool, c) Room service, d) Free Wi-Fi, e) Gym facilities. 
  • How satisfied are you with our room service’s responsiveness? Please rate your satisfaction on a scale of 1-10. 

3. Retail industry research:

  • Which of the following factors influences your purchase decision? Please rank them in the order of most important to least important. a) Price, b) Style, c) Product quality, d) Brand reputation. 
  • How likely are you to visit our retail store for the upcoming New Year sale?

3. Food and beverage industry research:

  • Rate your satisfaction level on a scale of 1-5 with the taste of your meal.
  • How likely are you to recommend our establishment to others on a scale of 1-5?

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Ordinal scale in research is used to collect data so that researchers can infer conclusions. It helps us infer how many respondents are satisfied or dissatisfied with your services. The ordinal scale does not offer the reason behind the response. 

However, the ordinal scale can help you gauge customer response quickly and collect data fast. You can create short surveys using an ordinal scale for initial research using online survey tools and also automate the analysis of the incoming data. 

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