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Nominal Scale Vs Ordinal Scale

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Two of the four levels of measurements are the Nominal Scale and Ordinal Scale. In statistical analysis, there are four levels of measurement to gauge the variables. These measurement scales can be categorized as qualitative and quantitative data. 

The nominal scale and Ordinal scale are the 1st and 2nd level used in surveys, polls, and other statistical analysis in the field of market research analysis. 

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Nominal Scale

The term nominal originates from the Latin word “nomen” and “nominalis” which implies the meaning “name”. Following the meaning, the nominal scale categorizes variables into distinct classification. The category is based on nomenclature and not on ranks or orders. 

The numbers associated with the variables on a nominal scale are used solely to classify the data; it does not indicate rank or order.

For example, in the case of classification of gender in a survey, the responder selects the variable and not the number. 

  1. Male 
  2. Female
  3. Transgender

The numbers help to quantify the data for the final analysis and result. 

Nominal Scale Vs Ordinal Scale 1

Ordinal Scale

The ordinal scale is the opposite of the nominal scale because in this measurement scale the variables are arranged into ranks and orders. However, the scale is simply used to put the variables into ranks and not examine the degree of difference between the variables.

For example, let’s say you went to a drama theatre and you are asked to fill a survey

On a scale of 1 -5, how much did you like the drama?

  1. Extremely satisfied
  2. Satisfied
  3. Neutral
  4. Unsatisfied
  5. Extremely unsatisfied

The numbers indicate the rank they are used to put the variables into order. 

Nominal Scale Vs Ordinal Scale 3
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Let’s say sweet, salty, and spicy when considered individually fall into the category of Nominal Scale. 

However, when they are placed on the scale and put into order: Very Sweet, sweet, very salty, salty, very spicy, and spicy, they fall under the Ordinal Scale.


Nominal Scale: Gender, marital status, religion, race, hair color, country, etc are examples of Nominal Scale. They are all examples of the noun which do not require rank or order. 

What is your country of origin?

    • America
    • China
    • India
    • Australia
    • New Zealand

Ordinal Scale: Ranks, customer satisfaction rating and degree, socio-economic status, education qualification, etc. are examples of the Ordinal Scale.

How happy are you with our courier service?

    • Extremely happy
    • Happy
    • Neutral
    • Unhappy
    • Extremely unhappy


Nominal Scale

The variables in the nominal scale have no quantitative value associated with them. The variables are attributes and there is no need for arranging them in order or hierarchy. 

Since the numbers associated are only used for data collection these data are grouped into categories. The data in the nominal scale are calculated by percentage or mode of distribution. You can also use graphical interpretation such as pie charts and bar charts to represent nominal data.

Four types of tests can be used to examine nominal scale data. These are, 

    • McNemar Test
    • Cochran’s Q Test
    • Fisher’s Exact Test
    • Chi-Square Test

Ordinal Scale

The variables in ordinal scale are associated with numbers and sometimes we assign quantitative values. Although no arithmetic methods can be used to analyze ordinal data, you can use mode, median, and percentiles to measure the data. Table charts and mosaic plots can also be useful to interpret the ordinal data.

Non-parametric methods are also methods that can be used to measure ordinal data:

    • Wilcoxon signed-rank Test
    • Friedman 2-way ANOVA
    • Wilcoxon rank-sum Test
    • Kruskal-Wallis 1-way Test

Collection of Data

Nominal Scale

In order to collect nominal data, you can use a survey with questionnaires that include open-ended, close-ended, and multiple-choice questions. Data in nominal scale are descriptive in nature. The responses explain attributes or qualities. 

Open-ended and multiple questions can give the responder freedom to put forth their perspective and not restrict them to a specific option. 

  1. What is your view about the character design in our new video-game?


Nominal Scale Vs Ordinal Scale 2

Ordinal Scale

Variables and options in ordinal scale require to be put into rank and hierarchy. Surveys that include scales such as the Likert scale, rating scale, and other such scales that can classify the options into ranks are better used to collect ordinal data. 

Rating Scale: On a scale of 1 to 10 how much did you like the movie? (1 indicating bad and 10 indicating good)

Nominal Scale Vs Ordinal Scale 4


Nominal Scale

  • Let’s say you need to gather information about all the employees in your company, a nominal scale can be of great help. Asking questions related to all the general information you can create a survey. 
  • A nominal scale is ideal to collect data of people, things, or places. For example, a store can collect personal data of their customer, such as name, phone number, and email to use later for promotion or another purpose. 
  • You may as well have given such information in restaurants and malls. Such data is an example of a nominal scale.

Ordinal Scale

  • The ordinal Scale on the other hand is used to collect feedback, reviews, or ratings after a customer’s experience. Surveys asking questions about satisfaction, frequency, importance, the likelihood of recommendation, etc. are examples of the Ordinal Scale.
  • After purchasing something online or ordering food online, you may have received an email asking about your experience. 

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