**Nominal, Ordinal, Interval & Ratio Data**

### Levels Of Measurement: Explained Simply (With Examples)

*By: Derek Jansen (MBA) | November 2020*

If you’re new to the world of quantitative data analysis and statistics, you’ve most likely run into the **four horsemen of levels of measurement**: **nominal, ordinal, interval and ratio**. And if you’ve landed here, you’re probably a little confused or uncertain about them.

Don’t stress – in this post, we’ll explain nominal, ordinal, interval and ratio levels of measurement in **simple terms**, with **loads of practical examples**.

**Overview: Levels of measurement**

Here’s what we’ll be covering in this post. Click to skip directly to that section.

**Levels of Measurement 101**

When you’re collecting survey data (or, really any kind of quantitative data) for your research project, you’re going to land up with two types of data – **categorical** and/or **numerical**. These reflect different levels of measurement.

**Categorical data** is data that reflect characteristics or categories (no big surprise there!). For example, categorical data could include variables such as gender, hair colour, ethnicity, coffee preference, etc. In other words, categorical data is essentially a way of assigning numbers to qualitative data (e.g. 1 for male, 2 for female, and so on).

**Numerical data**, on the other hand, reflects data that are inherently numbers-based and quantitative in nature. For example, age, height, weight. In other words, these are things that are naturally measured as numbers (i.e. they’re quantitative), as opposed to categorical data (which involves assigning numbers to qualitative characteristics or groups).

Within each of these two main categories, there are two levels of measurement:

**Categorical**data – nominal and ordinal**Numerical**data – interval and ratio

Let’s take look at each of these, along with some practical examples.

**What is nominal data?**

As we’ve discussed, nominal data is a categorical data type, so it describes qualitative characteristics or groups, with no order or rank between categories. Examples of nominal data include:

- Gender, ethnicity, eye colour, blood type
- Brand of refrigerator/motor vehicle/television owned
- Political candidate preference, shampoo preference, favourite meal

In all of these examples, the data options are **categorical**, and there’s **no ranking or natural order**. In other words, they all have the same value – one is not ranked above another. So, you can view nominal data as **the most basic level of measurement**, reflecting categories with no rank or order involved.

**What is ordinal data?**

Ordinal data kicks things up a notch. It’s the same as nominal data in that it’s looking at categories, but unlike nominal data, there is also a meaningful order or rank between the options. Here are some examples of ordinal data:

- Income level (e.g. low income, middle income, high income)
- Level of agreement (e.g. strongly disagree, disagree, neutral, agree, strongly agree)
- Political orientation (e.g. far left, left, centre, right, far right)

As you can see in these examples, all the options are still categories, but there is an ordering or ranking difference between the options. You can’t numerically measure the differences between the options (because they are categories, after all), but you can order and/or logically rank them. So, you can view ordinal as a slightly more sophisticated level of measurement than nominal.

**What is interval data?**

As we discussed earlier, interval data are a **numerical data** type. In other words, it’s a level of measurement that involves data that’s **naturally quantitative** (is usually measured in numbers). Specifically, interval data has an order (like ordinal data), plus the spaces between measurement points are **equal** (unlike ordinal data).

Sounds a bit fluffy and conceptual? Let’s take a look at some examples of interval data:

- Credit scores (300 – 850)
- GMAT scores (200 – 800)
- IQ scores
- The temperature in Fahrenheit

Importantly, in all of these examples of interval data, the **data points are numerical**, but the **zero point is arbitrary**. For example, a temperature of zero degrees Fahrenheit doesn’t mean that there is no temperature (or no heat at all) – it just means the temperature is 10 degrees less the 10. Similarly, you cannot achieve a zero credit score or GMAT score.

In other words, interval data is a level of measurement that’s **numerical** (and you can measure the distance between points), but that **doesn’t have a meaningful zero point** – the zero is arbitrary.

Long story short – interval-type data offers a **more sophisticated level** of measurement than nominal and ordinal data, but it’s still not perfect. Enter, ratio data…

**What is ratio data?**

Ratio-type data is the most sophisticated level of measurement. Like interval data, it is **ordered/ranked** and the numerical distance between points is consistent (and can be measured). But what makes it the king of measurement is that the **zero point reflects an absolute zero** (unlike interval data’s arbitrary zero point). In other words, a measurement of zero means that there is nothing of that variable. Here are some examples of ratio data:

- Weight, height, or length
- The temperature in Kelvin (since zero Kelvin means zero heat)
- Length of time/duration (e.g. seconds, minutes, hours)

In all of these examples, you can see that the **zero point is absolute**. For example, zero seconds quite literally means zero duration. Similarly, zero weight means weightless. It’s not some arbitrary number. This is what makes ratio-type data the most sophisticated level of measurement.

With ratio data, not only can you meaningfully measure distances between data points (i.e. add and subtract) – you can also **meaningfully multiply and divide**. For example, 20 minutes is indeed twice as much time as 10 minutes. You couldn’t do that with credit scores (i.e. interval data), as there’s no such thing as a zero credit score. This is why **ratio data is king** in the land of measurement levels.

**Why does it matter?**

At this point, you’re probably thinking, “Well that’s some lovely nit-picking nerdery there, Derek – but why does it matter?”. That’s a good question. And there’s a **good answer**.

The reason it’s important to understand the levels of measurement in your data – nominal, ordinal, interval and ratio – is because they **directly impact which statistical techniques you can use** in your analysis. Each statistical test only works with certain types of data. Some techniques work with **categorical data** (i.e. nominal or ordinal data), while others work with **numerical data** (i.e. interval or ratio data) – and some work with a **mix**. While statistical software like SPSS or R might “let” you run the test with the wrong type of data, your **results will be flawed at best**, and meaningless at worst.

The takeaway – make sure **you understand the differences** between the various levels of measurement before you decide on your statistical analysis techniques. Even better, think about what type of data you want to collect **at the survey design stage** (and design your survey accordingly) so that you can run the most sophisticated statistical analyses once you’ve got your data.

**which statistical techniques**you can use in your analysis, so make sure you always classify your data before you apply any given technique.