During statistical observations, we get results we have to present decently. The method of the presentation depends on various factors such as the type of the observed data: discrete or continuous random variables.

The level of measurement or scale of measure is a classification that describes the nature of information within the numbers assigned to variables.

American Psychologist **Stanley Smith Stevens** developed **the most popular classification with four levels:** nominal, ordinal, interval, and ratio.

In this article, we will deal with Steven’s classic measurement scales and investigate the categories he described.

## Properties of Measurement Scales

Just before we dive into the topic, we have to understand the different **properties** of measurement scales. Knowing these can help you to better understand the nature of your variables and data.

### Identity

Each value on the scale has a **unique meaning** (e.g. 0 false , 1 true)

### Magnitude

There is an **ordered relationship** between the scale values. Some elements are bigger, and some elements are smaller than others.

### Equal Units

The units are **equally divided** along the scale. For example, the difference between 2 and 4 is equal to the difference between 6 and 8.

### Location of Zero

Identifies the **zero** element as **minimum value**.

Now that we know the properties, let us take a closer look at the four different measurement scales.

## Nominal Scale

Nominal is the **easiest** of all the categories and the **lowest measurement level** by **statistical** viewpoint. There are **no mathematical relations** between the scale values.
Sometimes, in the case of nominal scales, we assign **numbers** to objects as a **label**. It is not the measure of quantity rather, it **measures the identity** and difference.

## Ordinal Scale

In the case of the ordinal scale, we can order the **measurements** in terms of “**greater than**”, “**less than**” or “**equal to**”. The ordinal scale is based on **rankings**. The **order matters**, but the differences between the variables do not. In the case of ordinal scales, we can use a **median**, but never use the mean because it is meaningless.

An ordinal scale can be used for **placing winners** of a race, sizes of **clothing** and so on.

## Interval Scale

The interval scale values have their own **identity** and **magnitude**, and the scale units are **equal**. An interval scale is **equally divided** along the scale **without** a predefined zero point.

The zero is **not the minimum** value of the scale. The **difference** between the neighboring points are **measurable**, so the difference in **temperature** for example between 10° F and 20° F is the same as the difference between 35° F and 45° F.

The most frequently used interval scales are the **Celsius** scale and the **Fahrenheit** scale.

## Ratio Scale

The ratio scale also has **equally spaced** units along the scale **but with an absolute zero** point. Zero is the **first element** of the scale. This type of scale is widely used in the **physical sciences** to measure mass, length, duration, and so on.

This scale has **all the properties** that we described at the beginning of this article. These properties ensure that we **execute all the operations**, which includes **addition**, **subtraction**, **multiplication**, and **division**. The absolute zero point allows us to **define** how many times greater Value A is than Value B.

The **Kelvin** temperature scale has a **true zero** point.

*Tip: AnswerMiner automatically detects data type and helps you to create the perfect visualizations by suggesting the best charts.*

## Summary Table

In the end, here is a **comparison table** to better understand the different scaling methods.