This post may contain paid links to my personal recommendations that help to support the site!

There are many different types of data, and it can be confusing to know which type to use when you’re working on a project.

In this blog post, I’ll discuss the different types of data and provide examples. By the end of this post, you’ll have a better understanding of the types of data and how to use them!

Let’s have a closer look at the different data types!

What Are The 4 Types of Data?

1. Nominal Data
2. Ordinal Data
3. Continuous Data
4. Discrete Data

Let’s jump in and find out more about each of them!

1. Nominal Data

Nominal data is used to classify or categorize items into different types or groups. It does not show any order or ranking, and it cannot be measured numerically.

Nominal data is a type of categorical data. All categorical data types tend to split up data into separate groups and are usually qualitative in nature.

Examples of nominal data include:

• types of food
• types of gender
• types of education
• types of religion

Nominal data is a common statistical data point used by many data scientists in scientific research. Since most data in statistics are divided among research groups of participants, nominal data contains important quantitative value to their data analysis.

Nominal data also do not allow any negative values.

2. Ordinal Data

Ordinal data are used to order or rank items into different types or groups. This type of data shows the relative position, but it cannot be measured numerically.

Ordinal data is another type of categorical data, like nominal data. It is used to classify items into categories where the category order has meaning (i.e., one type of food is better than another).

However, ordinal data have additional information on the ranks of their categories. This means that not all categories are made equal, and their order has some meaning or information to them.

Therefore, ordinal data is a type of qualitative data.

Examples of ordinal data include:

• levels of customer satisfaction (e.g., very satisfied, satisfied, neutral, dissatisfied, very dissatisfied)
• student grades (e.g., A, B, C, D, F)
• rankings in a competition (e.g., 1st place, 2nd place, 3rd place)

Ordinal data is another key type used to generate summary statistics further down the scientific research plan.

For example, many qualitative research studies require participants to score against interval scales. The data collected in those cases would be ordinal in nature.

Nominal data also do not allow any negative values.

3. Continuous Data

Continuous data are used to measure variables that can be expressed as any fractional number. It shows intervals between values, and it is measured numerically.

Continuous data is one of the data types we use very frequently since it is a type of quantitative data. As such, it can be used to measure numerical data like types of variables that have decimal points.

Examples of continuous data include:

• temperature (e.g., 20 degrees Celsius)
• height (e.g., 180 cm)
• weight (e.g., 78 kg)
• speed (e.g., 10 km/h)

Continuous data is typically collected from devices that can measure minute changes and differences.

This makes it great for use cases where high sensitivity is essential. In the above examples provided, many of these data points are collected from sensors measuring natural forces.

Continuous data is commonly used in data science and statistical analysis as well. For example, a time series analysis can be done using continuous data.

4. Discrete Data

Discrete data are used to measure variables that have distinct values. It is measured numerically, but it only shows whole numbers or integers.

Examples of discrete data include:

• number of people (e.g., 10 people)
• number of cars (e.g., 5 cars)
• number of books (e.g., 20 books)
• number of days (e.g., 30 days)

Discrete data is one of the easiest data types to point out since we use them unknowingly each day. Our everyday objects, like the number of pens and pencils, count as discrete data!

In my experience as a data analyst, I was able to derive meaningful insights using only just discrete data. For example, I could provide a trend on the number of website visitors for a specific day. Once I combined that with some continuous data like time, I could plot out a nice bar graph to show a trend!

Related Questions

What is Quantitative Data?

Quantitative data is a type of numerical information that describes the characteristics of an object or phenomenon. It has a measurable value, meaning it can be expressed in numbers and tracked over time.

This data type is collected by calculating, counting, measuring, and recording different observations.

Examples include:

• temperature readings
• population size statistics
• sales metrics

What is Qualitative Data?

Qualitative data is a type of non-numerical information that describes the characteristics of an object or phenomenon.

This type of data is collected to gain an understanding and insights into a particular subject rather than measuring numerical values. It can be expressed in words, images, symbols, or gestures.

Examples include:

• customer feedback
• interviews
• surveys
• observations
• descriptive observations

What Is the Difference Between Qualitative and Quantitative Data?

The main difference between quantitative and qualitative data is that quantitative data is numerical, while qualitative data is non-numerical.

Quantitative data can be used to measure a particular object or phenomenon, whereas qualitative data provides more of an interpretive context. Furthermore, quantitative data is collected by measuring and counting, while qualitative data is collected by observing and interviewing.

Overall, quantitative data is better for measuring hard data, while qualitative data is better for understanding and interpreting the nuances of a particular subject or topic.

What Are The Types of Primary Data?

Primary data types are those collected directly from the source. These types of data include surveys, interviews, observations, and focus groups.

What Are The Types of Secondary Data?

Secondary data types are collected from existing sources, such as newspapers, books, websites, and government records. These types of data are gathered indirectly and may be less reliable.

What is Ratio Data?

Ratio data is a type of numerical data that has a fixed zero point, making it possible to compare values. It also has an absolute value and no negative numbers.

Examples include:

• height measurements
• distance measurements
• weight measurements
• time measurements

What is Interval Data?

Interval data is a type of numerical data that has a fixed zero point but does not have an absolute value. This makes it possible to measure the difference between two values but not their ratio.

Examples include:

• temperature measurements
• scores on psychological tests
• time measurements in hours/minutes/seconds.

Final Thoughts

By using types of data, researchers are able to gain a better understanding of the objects and phenomena they’re studying. This enables them to make more informed decisions and arrive at accurate conclusions.

Different types of data are used for different types of analysis and experiments, so it’s important to understand the differences between them.

By understanding types of data and when to use them, you can make sure that your research is accurate and that you are able to draw valid conclusions from it.

I hope this blog post has helped you understand the types of data and their uses better!