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Nominal Scale Vs. Ordinal Scale: Know The Differences

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Nominal scales and ordinal scales are the two most popular scales used in surveys to gather feedback from respondents. In statistical analysis, there are four levels of measurement to gauge the variables. These measurement scales can be categorized as qualitative and quantitative data. 

 

Nominal scale and ordinal scale are the 1st and 2nd levels used in surveys, polls, and other statistical analyses in the field of market research analysis. 

In this blog, we’ll look at how the nominal scale compares with the ordinal scale, the differences between them, and more. 

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What are a nominal scale and an ordinal scale?

To understand the two most used scales of measurement, let’s explore the definitions of nominal and ordinal scales. 

Nominal scale:

The term nominal originates from the Latin words “nomen” and “nominalis,” which implies the meaning “name.” Following the meaning, the nominal scale categorizes variables into distinct classifications. 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 the classification of gender in a survey, the responder selects the variable and not the number. 

What is your gender identification?

  • Male 
  • Female
  • Transgender
  • Prefer not to say
  • Others

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

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

  • Extremely satisfied
  • Satisfied
  • Neutral
  • Unsatisfied
  • Extremely unsatisfied

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

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.

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Nominal Scale Vs Ordinal Scale 1

Nominal vs ordinal scale: Examples

Nominal scale question example

Gender, marital status, religion, race, hair color, country, etc are examples of Nominal Scale. They are all examples of nouns which do not require rank or order. What is your country of origin?

  • America
  • China
  • India
  • Australia
  • New Zealand

Ordinal scale question example: 

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

How happy are you with our courier service?

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

 

Nominal Scale Vs Ordinal Scale 3

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.

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Differences - ordinal vs. nominal scale

In this section, we will explore the way ordinal and nominal scales differ in three categories – measurement, collection of data, and uses. 

1. Measurement

Nominal Scale

The variables in the nominal scale have no quantitative value associated with them. The variables are attributes, and there is no need to arrange 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 the 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

2. 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?

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).

3. Uses 

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.  

A nominal scale is ideal to collect data on people, things, or places. For example, a store can collect the personal data of its customers, 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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Summarizing the differences between ordinal and nominal scales

Here, let’s summarize the characteristics that sets the two measurement scales apart. 

Nominal Scale:

  • Nominal scale categories do not reflect any inherent order or ranking, they are purely for identification purposes. 
  • There is no overlapping between the categories, and each data point is mutually exclusive. 
  • The data analysis for this measurement scale is limited to basic measures like frequencies and percentages. 
  • The categories on a nominal scale have equal importance, this means there is no implication that one category is “better” than the other. 

Ordinal Scale:

  • Ordinal scale categories reflect specific order or ranking, indicating relative positioning. 
  • The intervals between the categories are not uniform, which also limits statistical analysis, such as mean and standard deviation. 
  • There is a sense of relative distance between categories, which means a higher rank indicates a higher position. 
  • Ordinal data analysis allows for statistical analysis like median, mode, and non-parametric tests. 

By understanding these nuanced differences, you can make informed decisions when selecting and interpreting the survey results. 

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