Statistics for Dummies: 102

6 min readFeb 12, 2023

Types of Data: Nominal, Ordinal, Interval, and Ratio Scales

This is the second article in the statistics series. I will be explaining types of data in detail and a way which is simple to understand. Dummies, pay attention!
I am always open to feedback. Feel free to post any questions, suggestions or something you’d like to see an article about, in the comments section below. You can access the links to the other articles in the series in the Journey Links section at the bottom.

In this article:
Introduction
Four Types of Data Scales
Understanding Nominal Data
Decoding Ordinal Data
Interpreting Interval Data
Analyzing Ratio Data
The Importance of Knowing the Type of Data Scale in Statistical Analysis
A Guide to Navigating the World of Data Scales
Conclusion
Journey Links

Introduction

You may be thinking about data, but are you sure you’re thinking about it correctly? In this post, we’ll demystify the way data is measured and the different types of scales. You’ll learn how to read charts and graphs more effectively, find the most appropriate statistical method for analyzing your data, and ensure that your results accurately reflect what’s going on in the world around you.

Four Types of Data Scales

The four types of data scales are nominal, ordinal, interval and ratio.

Nominal data is categorical data with no order or rank. For example: “What color is your car?” Ordinal data is categorical with a specific order but no meaningful differences between values within each category (e.g., first place vs second place). Interval data has consistent differences between values that can be measured numerically but doesn’t have an absolute zero point (e.g., temperature). Ratio scales have both consistent differences between values as well as having an absolute zero point

Understanding Nominal Data: Categorical Data with No Order

Nominal data is the simplest type of measurement, and it has no order. Nominal scales are used when you’re interested in categorizing observations into different groups. For example, if you’re studying the relationship between eating habits and weight loss, you could use a nominal scale to record whether your participant eats breakfast every day or not (yes/no).

You should use a nominal scale when:

You want to group things together as opposed to measuring them quantitatively (i.e., ordinal or interval). For example, if you’re looking for differences between groups of people based on their gender or age rather than how tall they are (which would be an interval scale).
You don’t need precise measurements; just general categories from which conclusions can be drawn about trends within those categories — for example, asking people how long they’ve been married instead of recording their exact wedding date would provide more useful information if all else were equal because it wouldn’t matter whether one couple got married five years ago while another did so yesterday; only that both couples had been married at least once before!

Decoding Ordinal Data: Categorical Data with a Specific Order

Ordinal data is a type of categorical data that has a specific order, but does not have zero points. For example, if you were asked to rate your satisfaction with your job on a scale from 1 to 10, where 1 is very dissatisfied and 10 is very satisfied (and the numbers represent this), then these ratings would be ordinal data because they represent categories with an order.

If you wanted to convey this information using numbers instead of words or symbols (1–10), it would be called cardinal scaling instead because it uses zero points.

Interpreting Interval Data: Numerical Data with a Consistent Difference but No Absolute Zero

Interval data is numerical data with a consistent difference but no absolute zero. This means that the difference between two values is the same regardless of which value you start with. For example, if you were to measure someone’s height in inches and then measure their weight in pounds, the difference between those numbers would be consistent:

However, this does not mean that interval scales can’t show trends over time or across populations; it just means that there isn’t any true zero point on an interval scale like there is on ratio scales (e.g., dollars).

Analyzing Ratio Data: Numerical Data with a Consistent Difference and an Absolute Zero

Ratio data has an absolute zero and a consistent difference between values. The most accurate scale is ratio, which can be used to measure things like weight, height, and age. The most useful scales are interval and ratio because they are both accurate and precise (meaning they have consistent differences between values).

The Importance of Knowing the Type of Data Scale in Statistical Analysis

If you are conducting a statistical analysis, it is important to know what type of data scale your variables represent. Knowing this will help you determine whether or not your variable can be used in certain types of analyses. For example, if your variable represents ordinal data but you use an interval-level analysis on it (like t-tests), the results may not be valid because they do not properly reflect the true nature of your findings.

Nominal scales: These are categories with no inherent order or ranking (e.g., gender). They’re useful for grouping things that don’t need to be compared quantitatively but still have some sort of meaningful relationship between them (e.g., determining which patients need the most intensive care).
Ordinal scales: Also known as ranked categorical data; these are categories with some sort of meaningful ordering within each category that cannot be meaningfully compared across groups (e.g., rating movies from 1–10 where each number represents how much you liked them). You can use ordinal scales when comparing two items at once using just one variable such as “Which movie did I like better?” but not when calculating mean difference scores between multiple groups at once because there aren’t enough values between 1–10 available for each item being compared simultaneously

A Guide to Navigating the World of Data Scales

Knowing the type of data scale in your analysis is important.

There are four types of data scales: nominal, ordinal, interval and ratio. Nominal data has no order (e.g., gender), while ordinal measures have a specific order but do not allow for comparisons between categories (e.g., satisfaction with service). Interval-level measurement allows researchers to make comparisons between measurements on an absolute scale that takes into account differences in magnitude between values (e.g., temperature). Finally, ratio level measurements allow researchers to make comparisons across all possible values within a given range (e.g., height).

Conclusion

As we have seen, there is a wide variety of data scales used in the field of statistics. While each type has its own unique set of characteristics and uses, they all share one thing in common: they help us make sense of our world. By understanding these four different types of data scales — nominal, ordinal, interval and ratio — you will be able to analyze numbers more effectively and use them as tools for making decisions based on facts rather than assumptions or opinions alone!

Journey Links

I will keep updating the list here when new articles are published in the series. Keep an eye on it!