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Qualitative vs Quantitative Data – What’s the Difference?

March 4, 2019

qualitative vs quantitative data

Data analysis is broad, exploratory, and downright complex.

But when we take a step back and attempt to simplify data analysis, we can quickly see it boils down to two methodologies: Qualitative data and quantitative data. These two data types are quite different, yet, they make up all of the data that will ever be analyzed.

Before diving into data analytics, it’s important to understand the key differences between qualitative and quantitative data.

One type of data is objective, to-the-point, and conclusive. The other type is subjective, interpretive, and exploratory. So, which is which?

Now that we got the differences out of the way let’s dive into each type of data using real-world examples.

What is qualitative data?

Qualitative data is non-statistical and is typically unstructured or semi-structured. This data isn’t necessarily measured using hard numbers used to develop graphs and charts. Instead, it is categorized based on properties, attributes, labels, and other identifiers.

Qualitative data can be used to ask the question “why.” It is investigative and is often open-ended until further research is conducted. Generating this data from qualitative research is used for theorizations, interpretations, developing hypotheses, and initial understandings.

Qualitative data can be generated through:

  • Texts and documents
  • Audio and video recordings
  • Interview transcripts and focus groups
  • Observations and notes

Surprisingly enough, identification numbers like a social security number (SSN) or driver’s license are also considered qualitative because they're categorical and unique to one person.

Qualitative data examples

To better understand qualitative data, let's take the example of a bookcase. The following characteristics of this bookcase determine the quality of the information that's available to us about it:

  • Made of wood
  • Built in Italy
  • Deep brown
  • Golden knobs
  • Smooth finish
  • Made of oak

what is qualitative data

When discussing qualitative data, we talk about a specific object’s characteristics. Qualitative data is derived through qualitative analysis of the detailed information about the matter at hand. 

With a bookcase, there are many ways to get different qualitative information from different observers. If someone doesn't have specific details on the bookcase, they can assume that it's made of teak, not oak and that it has a rough surface, not smooth. Likewise, we can also discuss what type of oak it's made of or what part of Italy it came from.

Thus, qualitative data identifiers can be subjective, making qualitative data analysis a complex process with numerous possibilities and structures.

What is quantitative data?

Contrary to qualitative data, quantitative data is statistical and is typically structured in nature – meaning it is more rigid and defined. This data type is measured using numbers and values, making it a more suitable candidate for data analysis.

Whereas qualitative is open for exploration, quantitative data is much more concise and close-ended. It can be used to ask the questions “how much” or “how many,” followed by conclusive information.

Quantitative data can be generated through:

  • Tests
  • Experiments
  • Surveys
  • Market reports
  • Metrics

Types of quantitative data and examples

Quantitative data can be broken into further sub-categories. These categories are called discrete and continuous data.

what is quantitative data

Discrete data

Discrete data is just data that cannot be broken down into smaller parts. This type of data consists of integers (positive and negative numbers, e.g., -100, 10, 100, and so on) and is finite (meaning it reaches a limit).

A few examples of discrete data would be how much change you have in your pocket, how many iPhones were sold last year, and how much traffic came to your website today.

Another important note is that discrete data can technically be categorical. For example, the number of baseball players in a team born in Mexico is whole and discrete.

Continuous data

Continuous data is data that can be infinitely broken down into smaller parts or data that continuously fluctuates.

A few examples of continuous data would be the speed of your train during the morning commute, the time you take to write an article, your weight, and your age.

Which data type is better for data analysis?

Now that we have broken down the bread and butter of qualitative and quantitative data, it’s time to consider which type is better for data analysis.

Qualitative data will almost always be considered unstructured data or semi-structured. This type of data is loosely formatted with very little structure. Because of this, qualitative data cannot be collected and analyzed using conventional methods.

Making sense of qualitative data can be time-consuming and expensive, although some methods of “structuring” this data.

For example, one could apply metadata to describe an unstructured data file. Alt-text is a type of metadata applied to image files to assist search engines like Google, Bing, and Yahoo with indexing relevant images.

The rise of NoSQL databases has made the collection and storing of qualitative data much more manageable. However, it’s still an upward climb before being able to harness this data completely.

Quantitative data will almost always be considered structured data. This type of data is formatted in a way so it can be quickly organized and searchable within relational databases. Perhaps the most common example of structured data is numbers and values found in spreadsheets.

Because quantitative data and structured data go hand-in-hand, quantitative data is generally preferred for data analysis.

Quantitative vs. qualitative research

Quantitative research is a numerical and statistical analysis of statistical and numerical data (numbers and statistics). On the other hand, qualitative research deals with open-ended and non-numerical data (concepts, descriptions, meanings, words, and more).

Types of qualitative research methods

Qualitative research methods focus on qualitative data collection methods to study and observe a target audience and draw conclusions from the data collected. This research method relies more on subjective characteristics determined by qualitative approaches. Here are some of the commonly used qualitative research methods.

Focus groups

The focus group research method is one of the most widely used qualitative research methods in data collection. A focus group consists of a certain number of respondents (5-10) who act as a cluster. You can send an online survey or questionnaire to a focus group to get their feedback or opinion on a specific issue and record their responses for quick data analysis.

Forming and managing focus groups can be a complex and costly process. It's used by companies that may want to launch a new product to study the market and gain insight into their target audience's needs, preferences, and behavioral traits.

Ethnographic research

Ethnographic research involves observing and studying research topics in a specific geographic location. This geographic location can range from a small entity to a large country in the world. Ethnographic research helps study cultures, behaviors, trends, and pain points in a natural setting. Again, this is a time-consuming and challenging research method and cannot be limited to a specific period. 

Case study

A case study is an in-depth analysis primarily used in the social and educational sciences. It can be done by any organization, large or small, to draw a detailed inference on a topic. A case study is also one of the most straightforward but most time-consuming research methods that involve data collection from multiple sources.


The narrative research method relies on developing or creating a story. You put together a series of events and create a narrative around an observed topic. This investigation method shows how a small entity can affect a significant event and determine its relationship.


Phenomenology is another research method that takes a specific event or phenomenon into account for qualitative data collection. It helps an observer understand how, why, and what phenomenon occurs and its impact on the issue at hand. Phenomenology uses various data collection methods, from documents, videos, interviews to visiting a place, experiencing the phenomenon, and describing how it has evolved and developed over time.

Grounded theory

Grounded theory focuses on explaining the reasoning behind an event's occurrence. The sample size used for grounded theory is usually below a hundred subjects and evaluates how something happens at a given point in time. 

Types of quantitative research methods

Quantitative research methods, on the other hand, use quantitative approaches for data collection and analysis. It is a numerical and statistical research method that uses numbers and statistics for reasonable and definite deductions. Here are some of the common quantitative research methods.


A correlation research method studies the relationship between two or more variables that are similar and interdependent. It shows how one variable affects the other and vice versa. Correlation research uses mathematical analysis to analyze collected data. The results can be presented in a diagram or generated in statistics to complete the research results.


An experimental research method is a theoretical research approach. It focuses on a theory to distinguish between right and wrong statements. This research method helps verify an argument and is mainly used in the natural sciences.


Casual-comparative compares two variables that are not related. One is dependent, and the other is independent. Also known as quasi-experimental research, it determines the cause-and-effect relationship between these variables.

Survey research

Survey research is one of the most widely used research methods in areas from education to politics to economics and more. Organizations use it to gather feedback on their products from random respondents through surveys, questionnaires, and other input methods. The data collected is then analyzed to obtain quantitative results.

How well do you know the difference?

To strengthen your understanding of qualitative and quantitative data, think of a few ways in your life where both can be applied.

Start with yourself as an example. To acquire qualitative data, consider identifiers like the color of your clothes, type of hair, and nose shape. For quantitative data, consider measurables like your height, weight, age, and shoe size.

With a firm grasp of qualitative and quantitative data, you can begin making sense of big data analytics.

big data analytics
Have a large dataset to deal with?

Use big data analytics software to extract understandable information from large data clusters.

big data analytics
Have a large dataset to deal with?

Use big data analytics software to extract understandable information from large data clusters.

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