Nominal vs. Ordinal

There are four levels of measurements: Nominal, Ordinal, Interval and Ratio. Nominal and Ordinal are qualitative data, whereas Interval and Ratio are quantitative data. In this article, we will discuss nominal vs. ordinal.

In statistics, we collect different types of information like name, age, location, address, products, price, etc. These are called variables, and these variables are classified into a different levels of measurement. There are four levels of measurement: Nominal, Ordinal, Interval, and Ratio. In this article, we will discuss Nominal vs. Ordinal.
Nominal and Ordinal data are categorical, while Interval and Ratio are classified into numerical.
Categorical data is a non-quantitative data type, i.e., you can’t perform an arithmetic operation on Nominal and Ordinal data types.

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Table of Content

• Difference between Nominal and Ordinal
• What is Nominal Data?
• What is Ordinal Data?
• Key Difference between Nominal and Ordinal Data

Difference between Nominal and Ordinal

Parameter	Nominal	Ordinal
Definition	Nominal data is defined as the data used for naming or labeling variables, without any quantitative value.	Similar to nominal data, ordinal data is categorical data with an order.
Uses	Used to collect data of people, places, or things.	Used to collect feedback, reviews, or ratings.
Degree of Quantitative Value	There is no quantitative value associated with variables. It is a qualitative measure.	Quantitative values are attached to ordinal variables, but you can’t conduct arithmetic calculations.
Mathematical Operation	Equality	Equality and Comparison
Measures of Central Tendency	Mode	Mode and Median
Measure of Variability	None	Range and Interquartile Range
Example	Sex (male and female)	Customer Satisfaction Rating (on a scale 0 – 5)

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What is Nominal Data?

The term ‘nominal’ comes from the Latin word ‘nomen’ or ‘nominalis’, which means name. It is defined as the data type that is used for naming or labeling variables.

• Classifies the data into named groups without any quantitative meaning.
  • i.e., it divides variables into mutually exclusive and labeled categories.
• Statistical tests like chi-square goodness of fit and chi-square test of independence are used to analyze the nominal data.
• Frequency distribution and mode are used to analyze nominal data.
• Nominal variables can be coded with numerical values (arbitrary order), but they can’t be used for arithmetic operations.
• Example:
  • Blood Type: A, B, O, AB
  • Sex: Male, Female
  • Marital Status: Single, Married, Divorced

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What is Ordinal Data?

Ordinal Data classifies variables that have a natural order or rank.

• Ordinal data can be presented in tabular and graphical format
• Frequency distribution, mode, median, and range are used to analyze Ordinal Data.
• Statistical tests like Spearman’s rho correlation test, mood’s median test, etc., are used to analyze the data.
• Example:
  • Grades: A, B, C, D, F
  • Rating: Worst, Poor, Neutral, Good, Excellent
  • Income Group: High, Medium, Low
  • Education: 12th, Graduate, Post-Graduate, PhD

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Key Difference between Nominal and Ordinal Data

• Nominal data is a group of non-parametric variables, whereas Ordinal data is a group of non-parametric ordered variables.
• Ordinal data is analyzed by mode, median, quartiles, and percentile, whereas nominal data is analyzed by grouping variables into categories and calculating the distribution mode.
• Nominal data are categorical, whereas Ordinal data are both categorical and quantitative.
• Both nominal and ordinal data can be analyzed using percentage and mode.

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Conclusion

In this article, we have discussed the difference between Nominal and Ordinal Data.

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