## There Are 3 Categorical Data Types - Do you Know Them All?

These days, pretty much everybody has to collect and analyse data, but strangely, few people know what the basic data types are. Most understand that you can have numerical data and categorical data, but not much more than that.

I can't stress how important it is to your whole study that you understand the different data types and what you can do with them, and despite being critically important, it's all so simple!

Not to worry, though, because I've got just the thing for you - I'm going to show you all the different categorical data types that you'll encounter, and I'm going to teach you how to recognise each of them!

### Choices, Choices...

And now I'm going to give you a choice...

Ooh, I like choices!

You can either watch the video below to learn more about the different categorical data types or you can read the text instead.

It's up to you...

And just so you know, this video lesson is actually one of the lessons in The Hive from our **exclusive** video course How to Analyse Categorical Survey Data in Excel and in R, which is Open Access to start with (you don't need to register).

If you want to continue learning, you can start from the beginning here:

##### VIDEO COURSE

### How to Analyse Categorical Survey Data in Excel and in R

### 3 Types of Categorical Data

Let's jump straight in!

There are 3 types of categorical data; **Ordinal** data, **Nominal** data and **Dichotomous** data, like this:

And the biggest assumption we make about these data is that the categories are mutually exclusive, meaning that a piece of data cannot belong to more than one category.

If you have a variable that lists all the animals on a farm with the categories of [Pig, Sheep, Goat], each animal can only be in one category - it has to be a pig, a sheep or a goat. An animal can't be both a pig and a sheep at the same time.

If you go out and buy a duck, you can either call it a pig, a sheep or a goat - it can't go into more than one category.

Alternatively you can create a new category!

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#### Ordinal Data

The one thing that distinguishes Ordinal data from the other types of categorical data is that **the categories are ordered** in some way.

Whether the categories are named (such as Small, Medium, Large) or numbered (1, 2, 3), there is a natural progression across the categories.

Here are some examples of Ordinal data categories:

One important thing to note is that **the categories do not have equal distance between them** (although *experienced* data analysts often treat them as if they do).

For example, the difference in size between Small and Medium is not the same as that between Medium and Large.

This also applies to numbered categories.

When your data are arranged into numerical categories, the distance from 1 to 2 may *look like* the same as the distance from 2 to 3, but it isn't.

Don't think you can get around this rule by numbering your categories instead of naming them!

#### Nominal Data

In contrast, the one thing that distinguishes Nominal data from the other types of categorical data is that **the categories are unordered**.

It doesn't matter whether the categories are named (such as Red, Green, Blue) or numbered (1, 2, 3), there is a **no natural progression** across the categories.

Here are some examples of Nominal data categories:

When your Nominal data are arranged into numerical categories [1, 2, 3] it may *appear* that there is an order, but if your data are Nominal, there isn't one.

You can't have categories of [Pig, Sheep, Goat], rename them as [1, 2, 3] and suddenly expect them to have an order.

If there isn't one, there isn't one - the categories are **named**, whatever the naming convention!

#### Dichotomous Data

The word Dichotomous comes from the Greek *dikhotomos* (from *dikho-* ‘in two’ + *temnein* ‘to cut’), meaning 'to cut in two'.

In terms of Dichotomous data, it means that we have two categories.

Examples of Dichotomous data are:

These 2 categories can be Ordinal (have order) or Nominal (no order) - and in terms of data analysis it doesn't matter because we treat them exactly the same anyway!

**Categorical Data Types **- Do you know them all? (video) #data #analysis @chi2innovations @eelrekab

### Categorical Data Types Summary

So there you have it - pretty simple stuff, but really, really important.

If you don't know whether your variables are Nominal, Ordinal or Dichotomous, how would you know which statistic to use?

Should it be a Chi-Squared Test or the more powerful Chi-Squared For Trend? Or maybe even a Fisher's Exact Test?

Should you use a Binary Logistic Regression, a Nominal Logistic Regression or an Ordinal Logistic Regression?

These are all questions for another day, but I hope you can see that knowing (and understanding) your categorical data type is muy importante!

**I appreciate that the different types of categorical data aren't the sexiest beasts on the planet, so if you've got this far without running away, well done!**

**Give yourself a boost by clicking our 'Awesome Button'!**

### How to Analyse Categorical Survey Data in Excel and in R

I hope you enjoyed this video lesson.

It's actually one of the lessons in The Hive from our **exclusive** video course How to Analyse Categorical Survey Data in Excel and in R , where I'll teach you everything you need to know about analysing categorical survey data.

This course is Open Access to start with (you don't need to register), and after you've gone so far through you'll need a Free Plan to continue.

As part of the course you'll get all the data and resources you need to practice with.

If you want to continue learning, you can start from the beginning here:

##### VIDEO COURSE

### How to Analyse Categorical Survey Data in Excel and in R

I look forward to seeing you on the inside!

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