# Data Analysis 101: Types of Data You Encounter in Data Analysis

Learn about the different categories of data that are used in data analysis, as well as how they are analyzed. Examples are included to further your understanding.

9 minute read.

In this second article of our series on Data Analysis 101, we will learn about the different types of data you will encounter in data analysis and how they are processed and analyzed, with some examples highlighting their use.

## Qualitative and quantitative data

Let’s consider a new user who landed in your online store and began browsing, eventually adding an item or two to their cart and then abandoning it. What data can we get from that user? Some of them are as follows:

• How the user discovered your online store
• The device the user used to browse your online store
• The browser app the user used to access your online store
• The date and time the user arrived at your online store
• The first page the user accessed in your online store
• How long the first page loaded in the user’s device
• How long the user read the first page they accessed
• The other pages the user accessed
• How long the other pages loaded in the user’s device
• The items added to cart by the user, and how many for each item
• The reported ratings of the items chosen by the user to add to their cart
• The browsing experience as perceived by the user (ranging from very satisfied to needs improvement)

How can we make sense of these data? The first step is by classifying them by their type.

There are two primary types of data in data analysis: qualitative and quantitative.

Qualitative data is a type of data that is represented by words or a set of numbers that serve as identification. Examples of qualitative data are name, place of residence, and sex. Qualitative data is categorized based on identifiers such as attributes, properties, type, etc.

From our example at the start of this article, we can identify the following as qualitative data:

• How the user discovered your online store
• The device the user used to browse your online store
• The browser app the user used to access your online store
• The pages the user accessed in your online store
• The other pages the user accessed
• The items added to cart by the user
• The reported ratings of the items chosen by the user to add to their cart
• The browsing experience as perceived by the user (ranging from very satisfied to needs improvement)

This information is useful because it gives understanding of what the user wants from your online store and also helps you identify patterns in their behavior, including possible reasons for cart abandonment. (Which data can help you with that?)

Quantitative data, on the other hand, is represented by numbers and is generated through an act of measurement or act of counting. Examples of quantitative data are your height and weight, your annual income, and the number of purchases you made. Statistical methods are often required to extract more information from quantitative data.

From our example at the start of this article, we can identify the following as quantitative data:

• The date and time the user arrived at your online store
• How long it took for the first page to load in the user’s device
• How long the user read the first page they accessed
• How long the user read the other pages accessed
• How long the other pages loaded in the user’s device
• How many items the user added to the cart and their total price

With proper data analysis, this data can help in pinpointing the page’s performance in retaining the user interest, the effectiveness of the page layout, and other related information. You can read our article about the page performance metrics here.

Which is the preferred type of data: qualitative or quantitative? Neither! They are equally important, and the type of data best collected depends on the questions you are asking. In fact, you need both types of data most of the time, as they complement each other in helping you see both the big picture and the tiny details of the situation.

## Qualitative: nominal and ordinal data

Let us now consider the two types of qualitative data: nominal and ordinal data.

Nominal data is the type of qualitative data that serves as a label. Examples are items ordered, state of residence, and the device used in browsing the online store.

Ordinal data is the type of qualitative data that implies an intrinsic order among the possible values. Examples are ratings (whether by words or by number of stars), time of the day, and anything that implies progression.

For our example above, we can see that the following are nominal data:

• How the user discovered your online store
• The device the user used to browse your online store
• The browser app the user used to access your online store
• The pages the user accessed in your online store
• The other pages the user accessed
• The items added to cart by the user

While the following are ordinal data:

• The reported ratings of the items chosen by the user to add to their cart
• The browsing experience as perceived by the user (ranging from very satisfied to needs improvement)

What kinds of analysis can we do with qualitative data? One is to check the frequency of each kind of response, and look for the most likely and least likely responses. If, for example, the majority of the users who browse your online store use mobile devices, you should conclude that ensuring the website works well in mobile phones should be the priority. Now if, for example, the majority of the feedback is that your website does not work on mobile devices, then you should start investing in a website that works well on mobile devices.

## Quantitative: interval and ratio data

Let us now consider the two types of quantitative data: interval and ratio data. To make things easier, let us focus on the difference between them. Interval data has no “true zero” while ratio data has a “true zero”.

A “true zero” means that a “zero” means “nothing”. One good example of this is height. Zero height implies that there is no object or person there.

So how does that fit with our two types of quantitative data? It means that for the case of interval data, the zero was set arbitrarily, while that isn’t the case for ratio data. Additionally, this means that interval data can have negative values while this isn’t true for ratio data. To drive this point further, examples of interval data are temperature in Fahrenheit and Celsius while examples of ratio data are height, age, and time interval as measured with a stopwatch.

Let us now go to our example. The following will fall under interval data type:

• The date and time the user arrived at your online store

While the following will fall under ratio data type:

• How long the first page loaded in the user’s device
• How long the user read the first page they accessed
• How long the user read the other pages accessed
• How long the other pages loaded in the user’s device
• How many items the user added to the cart and their total price

You can do more types of analysis with these data. One example of these are what is called in statistics as measures of central tendency (you might have heard them in grade school). The measures of central tendency are mean, median, and mode:

• The mean is also known as the arithmetic mean or the average, which is calculated by adding all the given values in the list divided by the quantity of such values.
• The median is the value at the middle of the list after arranging them by increasing order.
• The mode is the most frequent value in the list.

There is much more to unpack here, including when should they be used and when should they not be used. For now, however, this is just one example of what we can do with both types of quantitative data.

## Summary

We will encounter two basic types of data in data analysis: qualitative and quantitative data. Qualitative data is a type of data that is represented by words or by a set of numbers that serve as identification. Quantitative data, on the other hand, is represented by numbers and is generated through an act of measurement or an act of counting.There are two types of qualitative data: nominal data is the type of qualitative data that serves as a label or name to what it describes, while ordinal data is the type of qualitative data that implies an intrinsic order among the possible values. There are also two types of quantitative data, and the difference between them lies on whether a “true zero” exists or not.  Interval data has no “true zero” while ratio data does.

Knowing the types of data that you will encounter will help you in identifying the right types of analysis that you can apply to them. If you don’t want to do them by yourself, however, we have the Lido app.  Not only does it have integrations with several e-Commerce and marketing services and platforms, but Lido can also apply the proper types of analysis and give you the most relevant metrics that you need to acquire and retain customers. Learn more about the Lido app here

## References

The following sources were used as references:

Qualitative vs Quantitative Data – What's the Difference?

Types of Statistical Data: Numerical, Categorical, and Ordinal - dummies

Data Types in Statistics. Data Types are an important concept of… | by Niklas Donges

4 Types of Data in Statistics – Definitions, Uses & Examples

Types of Data in Statistics - Nominal, Ordinal, Interval, and Ratio Data Types Explained with Examples

Quantitative vs Qualitative Data- Definition, 13 Differences, Examples

Qualitative vs Quantitative Data:15 Key Differences Similarities

Understanding Qualitative, Quantitative, Attribute, Discrete, and Continuous Data Types

Mean, Mode and Median - Measures of Central Tendency - When to use with Different Types of Variable and Skewed Distributions

Measures of Central Tendency: Mean, Median, and Mode