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Nominal Scale

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Table of Contents

What is nominal scale?

Nominal Scale, unlike the other scales from the Four Levels of Measurement, uses “tags” or “labels” to associate value with the rank. It differentiates items based on the categories they belong to. A nominal scale does not depend on numbers because it deals with non-numeric attributes. It is a useful methodology found in market research software.

For instance, in a marathon race, all the contestants are given a number. These numbers are for the purpose of identifying the contestant. The numbers don’t have any association with the result of the race or with the characteristics of the person. 

 A nominal scale can have both, qualitative as well as quantitative variables. For instance, religious affiliation, gender, country or city you belong to, marital status, etc. can be considered to be a type of Nominal Scale. 

An example of a Nominal scale is:

What is your gender?

  • Male
  • Female
  • Other  

We come across nominal scale in our daily activity

  • For instance, when you are purchasing something online, you are needed to fill up a form.
  • Similarly, job applications, profiles in social media or on dating websites are examples of nominal data.

Characteristics of Nominal Scale

  1. Nominal scale gets its name from the Latin word “Nomen” which means “name”.
  2. The scale can assist in the classification of attributes that do not have any quantitative value. 
  3. Nominal scale, however, in certain cases may be eligible for qualitative and quantitative variables.
  4. The numbers on a nominal scale have no value and don’t affect the categories in any way.
Nominal Scale 14

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How to use Nominal Scale?

In order to collect data using the nominal scale, you have to use a question-type survey. Such questions could be of several kinds. Today’s best online survey tools have many options for nominal scale, of which some are listed below:

  • Open-ended Question 
  • Multiple responses Question
  • Close-Open ended Question

Open-ended Question: This question-type survey permits the respondent to answer freely.

 It may include a question followed by a black space to answer. For example,

  • What is your name?
  • Answer:

Or, it may include a question and then a list of options to select from.

  • What is your natural hair color?
  • Options: (List of natural hair color)

Multiple responses questions: This type of question allows the respondent to select more than one option as their choice of answer. However, you have the control to limit the number of choices a responder can give.

Such as, a question followed by “choose only two options”.

  • What is your favorite drink?
  • Cold Drink
  • Mocktails
  • Tea
  • Coffee
  • Juice
  • Health drink

Close-Open ended Question: This type of question pattern combines the features of both open-ended questions and multiple response questions. The “open-end” for these question types have the option of “others” for the responders to put their personal answer if their choice is not on the list.

For example:

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Analysis of Nominal Scale

For the fact that nominal scales are often qualitative in nature the issue of calculation and analysis is often confusing. The numbers assigned to the attributes have no numerical value. This means that no arithmetic computation can be performed on data obtained by nominal scale.

Mode or Percentage: Equality or set membership or grouping method can be used to analyze nominal data. After gathering the data, you can group them into separate categories. The mode or percentage of each category can then be calculated.

For instance, after collecting data based on the religious affiliation of all the people in your institute, you can categorize the responders into separate groups.

 After segmenting the responders you can quantify the percentage of each group against the total no. of participants.


On the other hand, median or mean cannot be calculated because they would make no sense. The qualities of the attribute can be put in any order since ranking the variables are meaningless. 

Graphical interpretation: Pie charts and bar charts are two techniques that are useful for an analysis of nominal data. 

Pie Chart: You can use a pie chart to represent the percentage value of your findings. 

Bar Chart: The height of each bar can represent the frequency of the categories according to the responses.

Categories of Nominal Scale

The data gathered after every survey requires to be grouped based on the characteristics. This helps the researchers to assess the analyzed data against the unanalyzed data.

There are two categories of assessing the nominal data. These are Matched Samples and Unmatched Samples.

The Matched Sample:

 In this category, the data with similar characteristics are paired together. The aim is to match each responder of the samples who share the same characteristics except for the one which is to be assessed. 

This helps in receiving better statistics by controlling the other unwanted variables of the data. 

For example, you can gather data on your employees who have contacted covid-19 by controlling the “viral flu” category after matching the list with the number of employees. 

The Matched category includes two types of tests.

  • McNemar Test: A distribution-free test
  • Cochran’s Q Test: Non-parametric test between a set of 3 or more groups.

The Unmatched Sample:

The category involves random pairs chosen for the purpose of analysis. Unlike, matched category this is an independent sample.

For example, when you want to find out the effects of hair oil and then you select 100 participants irrespective of their health condition.

The unmatched category includes two tests

  • Fisher’s Exact Test: The test helps find out if the proportion of one nominal variable is different among the values of another nominal variable.
  • Chi-Square Test:  This test helps check if the variables are different in each data.

 Nominal Scale can be utilized via online survey tools to collect data for the official purposes as wells as for surveys on a large scale.

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