Qualitative vs. Quantitative Research: What’s the Difference?

What is quantitative data?

You can express quantitative data in numerical values. It's about the statistical analysis of large amounts of data to discover patterns and trends.

Examples of quantitative data

The following kind of information yields quantitative data:

• Age
• Income
• Frequency of certain behaviors
• Views (e.g. of a video or post)
• Churn statistics

What is quantitative research?

The quantitative research definition comprises testing causal relationships between variables by compiling numerical data. Quantitative research is mainly used to

• Test or confirm theories and assumptions 
• Establish generalizations
• Describe variables

Here is an example of a survey with closed-ended questions. The data collected in a usability survey is purely quantitative.

As you can see, all questions ask respondents to choose a score on a rating scale. These answers can be displayed on charts and graphs like this:

Advantages of quantitative data research

The most significant advantage of quantitative research is how quick it is to collect a large amount of data. Quantitative studies are easy to replicate so that you can confirm theories without much effort.

It is based on grounded theory - consistent and systematic analysis. Thanks to this, the results are statistically reliable and objective.

Quantitative data analysis is also less error-prone than that of qualitative data. You can calculate results electronically, eliminating human error.

Disadvantages of quantitative data research

Of course, the main limitation of quantitative research is that you won't discover in-depth knowledge about the reasoning behind your results. You may also miss out on data you cannot predict plays a role in your research.

Asking for additional data is always a good idea. That’s why most of our templates include an open-ended question at the end, so respondents have the option to expand on their answers.

The downside is that analyzing open-ended answers is more complicated. That kind of analysis pertains to qualitative research.

What is qualitative data?

Qualitative data is information about the qualities or characteristics of people or phenomena. It is descriptive in nature and expressed in words and sentences rather than numbers.

Gathering qualitative data became popular when psychologists found they were unsatisfied with the scientific research model. They began conducting in-depth interviews and observing human behavior to fuel their studies.

Examples of qualitative data

The following kind of information yields qualitative data:

• Gender
• Beliefs
• The reasoning behind behaviors
• Views (e.g., religious or political)
• Reasons for churning

What is qualitative research?

The qualitative research definition is as follows: gathering and interpreting non-numerical data. Qualitative researchers seek in-depth insights on a given topic.

Advantages of qualitative data research

A qualitative researcher will likely uncover data unavailable in existing records or databases. The responses are highly contextual and subjective. Qualitative research allows respondents to have the freedom for varied and unexpected answers. 

Have a look at this CES survey, which collects both quantitative and qualitative data:

Disadvantages of qualitative data research

The most significant limitation of qualitative research is the time, effort, and costs involved. Following that, qualitative researchers generally do not conduct large-scale data collection.

Because samples are smaller, qualitative data often suffers the criticism of inadequate validity or reliability. Moreover, it is often impossible to replicate qualitative studies, so researchers cannot confirm their results through repetition.

And qualitative data is subjective. While it gives researchers the ability to gather insights they couldn't from quantitative data (i.e. unexpected answers rather than responses to multiple or single-choice questions), results are sometimes not representative and generalizations may be misleading.

The two research approaches also differ in collection methods and analysis. Let's look at those aspects in detail.

How to collect quantitative data?

The easiest way to collect quantitative data is through surveys and questionnaires.

Questions should have a multiple-choice or single-choice nature. This format allows for the generating of countable answers and numerical data. Even when the options are, for instance, “yes” and “no,” you can turn responses into quantifiable data.

This still gives you a lot of room to collect interesting insights from your respondents.

Survicate is a survey tool that allows you to choose from many question formats, including:

• Single answer
• Multiple choice
• Rating scale
• Smiley scale
• Dropdown list
• Matric
• Contact form
• Ranking answer
• And more

Which all product quantitative data. Survicate can then generate separate visual reports for each question in real-time.

You can also gather quantitative data through experiments and database reports, but those usually require a higher budget, more time, and more human resources.

How to collect qualitative data

Common qualitative methods of research include:

• Interviews
• Focus groups
• Documents
• Personal accounts or papers
• Cultural records
• Observation, surveys or questionnaires with open-ended questions

Interviews and focus groups are often loose in nature and unstructured. The researcher can pose open-ended questions and follow where the responses lead. This means no not applicable answers that can also skew results.

Collecting qualitative data with Survicate

In Saas business, researchers are often interested in user behavior. That's why it’s wise to pair your survey tool with a tool like FullStory, SessionCam, or Smartlook to analyze heatmaps and session recordings alongside survey results.

This mixed approach allows you to combine quantitative and qualitative data.

What is the difference between quantitative and qualitative research?

As mentioned above, quantitative studies concern numerical and measurable data. Quantitative data research is easily represented on graphs and charts.

Qualitative studies, on the other hand, usually result in personal accounts and texts that reflect people’s feelings or thoughts.

While quantitative research tests theories and hypotheses, qualitative research often leads to exploring or formulating them.

Mixed approach example

Let's say you are conducting a reason for churning survey. Without a template optimized for best results, you may end up with unreliable or inaccurate data.

Imagine answering the following single-choice question:

What is the reason for canceling your subscription?

and you were given the following choices:

• I found an alternative
• I can't afford it anymore
• Customer service was poor

but the real reason was that you no longer needed the tool. You could either fill out the survey with inaccurate data or abandon it. Both of these are less than ideal for the product researcher. The problem is that this survey is purely quantitative.

Solving the problem is introducing qualitative data collection. A survey must allow users to expand on their answer, as there is always the possibility that someone will have an answer unique to their situation the surveyor could not predict.

Survicate's templates are optimized for the best results

Have a look at Survicate's reason for the lost customer template.

Whatever answer is provided, the user has the option to expand. For example, if you were to choose "the price is too high," the survey logic shows you a follow-up question:

What would be the right price?

And instead of another list of closed choices, the customer may enter any appropriate amount for their use case. The product researcher can then perform better pricing research.

The template also includes an "other" option, where a customer may enter any reason for canceling. This means you don't have to create a long list of options that would ultimately be incomplete, as you simply can't predict every scenario.

And, either way, the open-text answer is skippable, so if a customer is satisfied with the closed-ended format, they don't need to expand.

Outcomes of quantitative and qualitative data collection

Qualitative research often answers the “why” behind a phenomenon. 

Analysis of qualitative data is based on summarizing, categorizing, and interpreting. Data is compared and evaluated based on different accounts. Researchers learn from details of testimonies from respondents, otherwise called “informants.”

On the other hand, quantitative research is about understanding the “what” behind a hypothesis. 

Analyzing quantitative data is easier and requires less effort than qualitative data. Answers are turned into numbers and statistics that you can use to fuel your business strategy.

Have a look at how this Customer Satisfaction survey. It compiles these two modes of data collection:

1. The first question will yield quantitative data. It asks the respondents to evaluate their level of satisfaction with a product, service, or company. The data is turned into numbers to produce bars and charts, which aid analysis.
2. The second question will yield qualitative data. Respondents can expand on their rating by providing text answers. Notice how a different question shows depending on the answer you provide. That is because the survey logic is set up that way. Researchers can learn both why customers are satisfied or unsatisfied.

Survicate's qualitative vs. quantitative data analysis

When you collect data with Survicate, closed answers are sorted based on the respondent's rating in the first question. That way, researchers can concentrate on positive and negative responses separately. 

Higher subscription tiers allow you to utilize the word cloud for text answers. This will make analyzing large amounts of qualitative data more accessible.

The Survicate word cloud is an interactive analysis tool. It shows respondents' most commonly used words in their text answers so that you can search for trends and patterns. You can also exclude words you don’t want to see in the word cloud.

Survicate integrations and the option of adding open-text questions mean you can use surveys to collect both quantitative and qualitative data from your customers or employees. 

But just collecting data is not enough. You must analyze and interpret the numbers and words to turn them into actionable roadmap tasks.

The FullStory, SessionCam, and Smartlook integrations are great for pairing responses with session recordings and heatmaps. Contextualizing qualitative and quantitative data will take your analysis to the next level.

When to use qualitative and quantitative research methods

You'll choose either quantitative or qualitative research depending on the type of study you want to perform and what you want to achieve. 

In simple terms:

• Use qualitative research to understand something, e.g., your users’ behavior. This research answers questions that begin with "why" or "how."
• Use quantitative research to confirm or test something, e.g., your users’ satisfaction

Usually, a mixed approach is best. Also, the larger your sample, the more accurate your findings will be. Use the best survey tools to ensure you can collect qualitative and quantitative data with as little effort as possible.

Check out this survey template, which collects both qualitative and quantitative data.

Use Survicate to perform quantitative and qualitative research

Ready to collect qualitative and quantitative data? With Survicate, you don’t have to invest to experiment with the available research methods.

Along with the collection and analysis of open-text answers, Survicate has many integrations that will give you the whole story for the perfect mix of quantitative and qualitative data.

For example, the Smartlook and FullStory integrations can give you an inside look behind closed answers with session replays. And you can use any of our integrations on our 10-day free trial!