# Levels of Measurements Basics for Every Data Scientist & Statistician

In the journey of statistics learning as a data scientist or statistician , It is really essential to understand the concept of Levels of Measurement . All the statistics is revolving around random variable and its distribution . But before understanding random variable distribution nature it is really essential to know how it is measured . This article ,”Levels of Measurement’s Basics for Every Data Scientist/ Statistician is full of such information . So Lets understand this part together .

## Levels of Measurement-

There are four ways by which you can measure your variable . Each one has its own importance . I know you want to know their name quickly . Here are 4 Levels of Measurements –

1. Nominal Level
2. Ordinal Level
3. Interval Level
4. Ratio Level

### Nominal Level

You can say nominal level as the categorical level of measurement. It has no numerical value and generally uses for classification of the data variable into categories. In addition, these variables cannot be sorted and are mutually exclusive with other variables. You can use it for labeling the variables in the dataset.

For example, Color is a nominal level of measurement. Red, Blue, green, e.t.c are part of the color category. Some of the other examples are:

Sex – Male or Female

Shoes – Casual, Running, sports, etc.

Country – India, USA, UK

### Ordinal Level

In the Ordinal Level of measurement, the variables can be orders and classified. It has non-numerical values and you will find a relationship among them. However, It can be ordered but lack any scale.

You will find in many survey questions, the ordinal level of measurement is used. For example, Satisfied, Unsatisfied, neutral, et.c is not numerical and can be ordered. You can say those variables that have no equivalent boundaries or distance between them, they are measured with the Ordinal level of measurement.

### Interval Level

With this level of measurement, data variable can be ordered into classified categories. In addition, it has no zero points. It means you can add or subtract two-interval level. But you can not multiply and divide it. It can also have negative values. You can find the difference between the two intervals that are the distance between the two intervals.

The common example of the interval level is the measurement of temperature. The difference between the 40 and 30-degree Celsius is the same as the difference between 70 and 60-degree Celsius that is 10.

### Ratio Level

You can consider this level as the father of all the above-described levels. It has all the characteristics of Nominal, Ordinal and Interval Level of measurements. In addition, it has no zero points. It means zero means real zero value no arbitrary zero. And also you can add, subtract, multiply, and divide it, there will be no change in ratio level.

For example, if the price of a product is 0. then it means that the product is zero value that is free. Another best example of ratio level of measurement is height and weight. If the weight f a person is zero then it means weight of that person is zero.

## Conclusion –

Almost every body who is reading this article , must priorly know about this (Levels of Measurements) . The idea is to give you knowledge about terminology (Levels of Measurements) . Because Going forward whether you read any article in Data Science Learner or any other place , You will get these term . Actually, it is standard practice. This is the basic building blocks .