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Qualitative vs quantitative research—what’s the difference?

6 minute read - 9 June 2018
Illustration of two people with a wallpaper of charts and graphs.

It’s important to understand the difference between qualitative and quantitative research, especially if you’re new to the field. There’s a common misconception that one is “better” than the other, however qualitative and quantitative research serve vastly different purposes. Read on to learn about what makes them different, the different methods for collecting qualitative and quantitative data, how you can turn one into the other, when you might use which method, and how to analyze the data once you have it.

Qualitative research

Qualitative (“qual”) research is often used for exploring. It helps researchers gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research.

Qualitative data collection methods

Qualitative data collection methods vary, and usually rely on unstructured or semi-structured techniques. Common methods include:

  • Focus groups

  • Individual interviews

  • Observation or immersion. For example, an ethnography

  • Diary studies

  • Literature reviews

  • Open-ended surveys and questionnaires

Using qualitative research methods, the sample size is typically small, and respondents are selected to fulfill a given quota.

Quantitative research

Quantitative (“quant”) research is used to quantify the problem by way of generating numerical data that can be transformed into useable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables, and generalize results from a larger sample population. Quantitative research uses measurable data to formulate facts and uncover patterns in research.

Quantitative data collection methods

Quantitative data collection methods are much more structured; they include:

  • Surveys. For example: online surveys, paper surveys, questionnaires, mobile surveys, and kiosk surveys

  • Interviews. For example: face-to-face

    interviews, telephone interviews, remote interviews

  • Longitudinal studies

  • Website interceptors

  • Online polls

  • Systematic observations

  • Experiments

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Qualitative vs quantitative data

In terms of the actual data, here are some of the key differences:

  • Qualitative data is not countable. It’s chunks of text, photos, videos, and so on

  • Quantitative data can be counted as it’s numerical

  • Qualitative data is usually unstructured, which means it’s not ordered or grouped logically. You can turn qualitative data into structured quantitative data through analysis methods like 

  • Most of the time qualitative data will be collected from a smaller sample size than quantitative data because, generally, you’re not looking for statistical significance with qualitative research

  • Qualitative data is quite rich, and can give you directional insights about people’s thoughts, feelings, emotions, and so on

  • Quantitative data can help to give you more confidence about a trend, and allow you to derive numerical facts

Imagine you’re looking down on a city from a helicopter at 5,000 feet. From here, you count all of the vehicles on a particular road, and conclude that 60 percent of vehicles are cars, 30 percent are trucks, and the rest are motorbikes. This would be a quantitative information. If you then landed on the ground and interviewed some motorbike riders about their thoughts on truck drivers, the notes or recording of those interviews would be qualitative data.

Turning qualitative data into quantitative data

You can turn qualitative data into quantitative data, and vice versa. They often blur, and you can represent the same data set in both ways.

Let’s consider a bunch of email conversations. In its raw form, this would be considered qualitative data. To answer the research question “what are the most popular greetings in emails?” you’d need to go through and sum all of the different occurrences of different greetings, then sort them by frequency. By doing this, you would have turned some unstructured qualitative data into a structured, countable insight.

How to analyze qualitative data and quantitative data

Once you have the data at your fingertips, you’ll need to learn how to analyze it in order to make meaning of it and answer your research questions.

Analyzing quantitative data

Because quantitative data is based on numbers, some form of mathematical analysis will be required. The methods range from simple maths like calculating means and medians, to more advanced statistical analysis like calculating the statistical significance of your results.

Tools like Excel, SPSS, or R can be used to calculate:

  • The mean scores of your data (also known as the average)

  • The frequency of a particular answer

  • The correlation or causation between two or more variables

  • The validity or statistical significance of your results

Analyzing qualitative data

Because of its unstructured and somewhat ambiguous nature, analyzing qualitative data involves a more interpretive style of analysis. This doesn’t mean it’s any less valid, but it does require the researcher to apply a strict analytical framework in order to derive significant meaning from the data.

There are many tools that help with the analysis of qualitative data, Dovetail being one of them.

Ways to analyze qualitative data:

  • Coding your data with tags and conducting a thematic analysis. With a tool like Dovetail you can assign “tags” to segments of your raw data to “code” it with a specific meaning. After you tag all your data, you can analyze the frequency of certain types of responses and identify patterns and themes.

  • You can also use coding for a range of other similar analysis techniques like content analysis

When to use qualitative vs quantitative research

Both types of research methods are useful for different reasons. A good way to think about this is by thinking of quantitative data as describing “what” your participants did, while qualitative data describes “why” they did it.

Quantitative research approach

Let’s consider an example. If you were to measure user behavior on a website, you might learn that 25 percent of people clicked on this button, then this button, and so on. That’s good to know, and you can run split tests (otherwise known as “A/B” or “multivariate” testing) to try out different versions of your implementation to see if you can change people’s behaviors for the better.

However, this data doesn’t provide information on why people did what they did.

Qualitative research approach

Qualitative research generally focuses more on the human angle—what are people thinking and feeling? What’s their rationale for doing something? What’s their attitude or perception of something? You can get much richer/deeper information with qualitative data, because you can actually understand the intent behind action, and not just see the result of it.

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So, let’s go back to our example—if you wanted to improve that website journey of someone clicking this button, then that button, and so on, then perhaps you’d observe your quantitative data to see what people are doing, and then you would run some qualitative research to try and learn why they are doing it.

When thinking of which method you should choose, you should consider your research questions, what type of research approach you're taking (for example, inductive vs deductive research), whether you're doing evaluative research like usability testing, or a generative research project that is more wide-ranging and exploratory. Often, you're able to employ a mixture of both qualitative and quantitative approaches to solve the problem at hand.

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