Data Types in Statistics: Nominal, Ordinal, Interval, and Ratio
Statistical data analysis involves the usage of certain statistical techniques that demands familiarisation of statistical concepts. There are many softwares, which can help you with this, but without understanding why something is happening, it is impossible to get considerable work done in statistics and data science.
Data Types are a vital concept of statistics, which needs to be understood, to correctly apply statistical measurements/techniques to your data and therefore to correctly come up with solutions to a certain problem. This blog post will introduce you to the different data types one needs to know, to do a proper exploratory data analysis (EDA), which is most of the times neglected.
Table of Contents:
- Introduction to Data Types
- Categorical Data (Nominal, Ordinal)
- Numerical Data (Discrete, Continuous, Interval, Ratio)
- Summary
Introduction to Data Types
Having a good understanding of the different data types, also called measurement scales, is a crucial prerequisite for doing Exploratory Data Analysis (EDA) since you can use certain statistical measurements only for specific data types. One should also need to know which data type he/she is dealing with to choose the right statistical methods.
In statistics, there are four data measurement scales: nominal, ordinal, interval, and ratio. These are simple ways to sub-categorize different types of data.
Categorical Data
Categorical data represents characteristics such as a person’s gender, hometown, or the genres of books they like. Categorical data can also take on numerical values (Example: 1 for female and 0 for male). Note that here “1" and “0" don’t have mathematical meaning.
Nominal data
Nominal data are used to label variables without any quantitative value. Common examples include male/female, hair color, nationalities, names of people, and so on.
In other words, they’re labels (and nominal comes from “name” to help you remember). You have brown hair (or brown eyes). You are Indian. Your name is Kiran.
Ordinal data
Here’s a trick to remember that ordinal sounds like order — and it’s the order of the variables which matters. Not so much the differences between those values.
Examples of ordinal scales are: measures of satisfaction, happiness, and so on. You must have taken one of those surveys, like this?
Numerical Data
1. Discrete Data
Discrete data represents items that can be counted but can’t be measured; they take on possible values that can be listed out. It may go from 0, 1, 2, on to infinity. It basically represents information that can be classified based on the category. Example: the number of heads in 100 coin flips.
2. Continuous Data
Continuous data represent measurements; their possible values cannot be counted and can only be described using intervals on the real number line. Like the weight of a person (calculated to decimal places), temperature (38.543 degrees, and so on), or the speed of a car.
Interval Data
The best example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 90 and 80 degrees is a measurable 10 degrees, as is the difference between 50 and 40 degrees.
If you need help remembering what interval scales are, just think about the meaning of interval: the space between the two extreme points. So not only do you care about the order of variables, but also consider the values in between the extreme values.
There is a little problem with intervals, however: there’s no “true zero.” A true zero has no value — there is none of that thing — but 0 degrees C definitely has a value: it’s quite chilly. You can also have negative numbers.
If you don’t have a true zero, you can’t calculate ratios. This means we can perform addition and subtraction, but division and multiplication don’t work.
Ratio Data
Ratio scales are the ultimate solution when it comes to data measurement scales because they tell us about the order, they tell us the exact value between units, and since they have an absolute zero, a wide range of both descriptive and inferential statistics to be applied. Good examples of ratio variables include height, weight, and duration.
Summary
In the above article, we learned about statistical data types like what are categorical data, numerical data. We also learned about nominal data, ordinal data, what is the difference between interval and ratio data, and how useful ratio data is when it comes to data measurement scales.
I hope you enjoyed the article and increased your knowledge about Data Types in Statistics.
Thanks for reading!
About the Author
Sreeshma is a B.Tech Computer Science grad from A P J Abdul Kalam Technological University, Kerala, India. She is an avid learner of Data Science and Machine Learning and aiming to carve a successful career in the data science field. She’s always open to suggestions, opportunities and new connections.
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