## Introduction

Measurement is assigning numbers to objects based on a set of rules. A scale is a device used to quantify or measure an event or an object.

In this article, we will learn four types of scales such as nominal, ordinal, interval and ratio scale. So without any further ado, let's get started!

## Data Measurement Scales

Variables or numbers are defined and categorised in statistics using various measurement scales. The multiple uses of statistical analysis are determined by the properties of each level of measurement scale.

Nominal, ordinal, interval and ratio are the four data measurement scales.

### Nominal Scale

Variables are labelled using nominal scales, which have no numerical value. In other words, we can say that the nominal scales assigns numbers where different numbers indicate different objects. "Labels" could be used to describe "nominal" scales.

Here are a few examples of nominal scale. These scales are mutually exclusive with no numerical significance.

Let us see different examples of Nominal scale:

- Gender is measured on a nominal scale. Individuals can be classified as "male" or "female," but neither value denotes greater or lesser "gender."
- A baseball uniform number is another example. The number doesn't reveal anything about the player's position or performance.

### Ordinal Scale

The ordinal scale assigns numbers to objects, but the numbers themselves have meaning. The order of the values is significant in ordinal scales, but the differences between them are not known.

Let's have a look at the following example:

- The rank (first, second, third, etc.) obtained by different players in a race indicates that the player in first place outperformed the player in second place, but it does not tell by how much.
- Ordinal scales are used to assess non-numerical concepts such as satisfaction, joy, irritation, etc.

### Interval Scale

Interval scales are numeric scales where we know the order of the values and the exact differences between them. Although numbers have been ordered, they also have equal intervals. Because the difference between each value is the same, Celsius temperature is a classic example of an interval scale. Interval scales are helpful because they expand the scope of statistical analysis on these data sets.

For example, the difference between 40 and 50 degrees and the difference between 80 and 90 degrees is a measurable 10 degrees.

### Ratio Scale

Ratio scales provide information about the order, the exact value between units, and an absolute zero, allowing for the application of a wide range of descriptive and inferential statistics. Everything said about interval data also applies to ratio scales, and ratio scales have a defined zero. Height, weight, and duration are all excellent examples of ratio variables.

Regarding statistical analysis, ratio scales open up a world of possibilities. These variables can be added, subtracted, multiplied, and divided in meaningful ways in ratios. The mode, median, or mean can be used to determine central tendency; ratio scales can also be used to calculate measures of dispersion such as standard deviation and coefficient of variation.

Examples of Ratio Scales

- 10lbs is twice as much as 5lbs(ratios are meaningful: 10/5=2)
- Zero pounds means no weight.

Let us have a look at a picture with the increasing levels of scales of measurement for a clear understanding: