Data is a significant part of the modern world. Businesses depend on it as it helps with most of the marketing-related decision making.

We have covered why data is so essential in our article about the importance of data analysis in the modern world.

Now let’s dig a little deeper.

Quantitative vs. Qualitative Types of Data

The first significant grouping when it comes to segmenting types of data is whether the information is numerical or not. This segmenting logic is quite straight forward:

Suppose you have data that is number-based, aka Numerical data, then you are dealing with quantitative data.

If you don’t have numbers in your data, you have categorical or qualitative data types.

It is easy to mix these two types of data up since the two words are very similar. The way I make this clear for myself is by thinking about the meaning. Quantitative indicates a quantity, which means how many of something. You would answer that with a number. Whereas, qualitative comes from the word quality, which would be hard to describe with a number. 

Another trick is to compare the words and see which one has the letter L in it. Think L for letters, so qualitative means this type of data is dealing with letters and not numbers.

Below are some examples of Quantitative and Qualitative Data types.

Quantitative Data  

• Ages of people
• Distances between cities
• Prices of products
• Temperatures in Celsius

Qualitative Data

• Gender
• Peoples names
• Categories of products
• Temperatures as descriptions – cold/hot

Quantitative Data Types – Discrete vs Continuous 

There is another layer of subcategories under the main types of data or the numerical or quantitative data sector. These two subcategories are discrete data and continuous data, which is also quite a straight forward classification.

Discrete Data – Think of this as a number for items that can’t be divided into smaller parts. 

For example; 

• the number of people in a room 
• the number of pets in your household 
• the number of chairs around the table 

In any of these scenarios, you can’t have increments in these numbers.

Continuous Data – keeps continuing and is not a fixed number. This type of data consists of numbers that can be broken into smaller and more precise units. Think about measurements, for example, I can state my height as 165cm, but I could go into more detail and add mm to it as well, taking it to 165.3cm. Time is another great example here because we can measure time in minutes, seconds or even to the nanoseconds.

Examples of Discrete and Continuous Data Types

Discrete Data 

• Number of coconuts on a palm tree
• Number of vacations in a year
• Number of players in a team
• Number times you went for a walk last week

Continuous Data

• Runners speed
• Temperature
• Volts of electricity
• Weight differences throughout the year

Continuous Data can be further divided into Interval and Ratio data types. And qualitative data can be further divided into Nominal and Ordinal data types.

These are classifications of data scales which I will not touch on in this article. 

Wrapping this up

A quick summary would be that the overall data is divided into two main types of data – numerical or not (qualitative data or quantitative data)

The above article is a very fundamental overview and covers the topic quite briefly. You can learn about the next level of subdivisions in our post about Data Measurement Scales and Different Types of Data

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