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It’s pretty important stuff. But before you dive in and start researching the heck out of everything, you'll need to decide on your approach. Usually, this boils down to choosing between qualitative and quantitative research or using a mix of both.
Don't worry if you need help figuring out where to start (or find saying qualitative and quantitative a bit of a mouthful). This article breaks down the key differences, so you know exactly which method to use and when.
Quantitative and qualitative methods are terms we use to describe broad research strategies. They don’t refer to specific data collection techniques or analysis methods, but they are useful in deciding how you plan to tackle your research.
So, first up, a definition of each of these research styles.
As the name suggests, quantitative research is based on numbers. It might appeal to the logical tendencies of left-brain thinkers.
Think percentages, statistics, population data, economic figures, or any other type of raw data that you can't dispute. It’s the brass-tacks of research methods.
A quantitative question gives you a black-and-white, often numerical answer. Quantitative answers might sound like this:
You could also do quantitative studies to test relationships between variables that aren’t necessarily numbers, like:
Quantitative research methods help you make predictions, draw conclusions from a larger audience, and identify patterns in their behaviour. If you’re working with concrete or numerical variables and data, odds are you’re doing quantitative research.
Qualitative research relies on non-numerical data such as language, concepts, beliefs, and trends. It moves away from raw data and focuses on things that are up for debate.
Here’s another way to think about it: if quantitative research is done with measurements, qualitative research is done with observations.
Qualitative questions allow you to dig deep into a topic, study the relationships between variables, and find potential patterns. Where we can use quantitative research to test hypotheses, we can depend on qualitative research to create them.
Qualitative research methods may uncover personal, difficult-to-measure attitudes and behaviours you wouldn't necessarily assign a number to. But as it's highly subjective, one researcher's interpretation of the data may differ entirely from another.
A qualitative research question might sound like this:
As you can see, these types of questions don’t have concrete, numerical answers. The researcher must sift through a goldmine of intel to uncover the unique perspectives of their research participants.
There is some overlap between quantitative and qualitative research. After all, they’re both driving forces behind evidence-based outcomes.
So how do you know when to use each research strategy? Here’s a good rule of thumb to follow if you’re stuck: use qualitative research to develop a theory, and use quantitative research to test it.
Want to understand the decision-making behind customer journeys? Or anticipate if your new product will be a hit? Qualitative research is the way to do it. It allows you to untangle complex topics by first observing, and then understanding the general mood or vibe that's otherwise challenging to quantify.
Qualitative insights help you spot issues that could otherwise be missed with raw data, like the unmet needs of potential customers. They can also pinpoint gaps in the market, help you understand how to tackle a policy change or uncover how competitors engage with their audience.
Sometimes your qualitative research will throw up ambiguities and contradictions. Not everyone will have the same opinion and it can be tricky to nail down the trends you're looking for.
This can make it seem like your qualitative data set doesn't make sense, but in actuality, your data probably just illustrates the complexities of social reality. Even if your qualitative studies give you mixed results, you’ll end up with a deeper understanding of whatever it is you’re studying.
There are a few biases to be aware of when working with qualitative questions. The validity of the research may be threatened by, including:
You can use quantitative methods to put your qualitative observations into context through measuring, counting, and analysing. If you suspect that a particular theory has merit, quantitative research can help you prove or disprove it.
It's an efficient way to measure the success of campaigns and strategies, test hypotheses, and take the guesswork out of decision-making processes.
Let's say you believe that you'll boost your business productivity by increasing employee retention. You may have come to this hypothesis based on qualitative survey questions you asked your team.
To test it, first look at your employee pulse surveys, performance reviews, exit interviews, and job satisfaction data. Then you can use this quantitative data to make more concrete predictions about how these variables might interact with each other in the future.
There are also biases to be aware of when doing quantitative research. These include:
A mixed methods-based research approach gives you the best of both worlds. There are two main ways to do it:
1. Qualitative followed by quantitative research: in this model, you'll use qualitative research to explore an idea and develop a set of hypotheses. Then you can use quantitative research to prove or disprove your theory (the way we outlined previously).
2. Quantitative followed by qualitative research: let’s say a stat has taken you by surprise. You’ll start by looking at the numerical values in question, and then use qualitative research to get a sense of what exactly is shaping those numbers.
Once you've chosen your approach, the next step is to determine the collection method for your quantitative or qualitative data.
Quantitative data works best when you gather data from a large sample set and a range of sources, including:
Qualitative data collection brings non-numerical evidence into the mix. Companies usually use a smaller sample size to save money, as this type of research requires plenty of time and resources.
Along with text-based research, you can also work from photos, videos, and audio recordings. Your data might come from sources such as:
Unanalysed data is like a stack of bricks without a builder—not much use. It’s important to learn how to turn raw data into valuable insights based on your research style.
Since quantitative data is numerical, it’s a great idea to use spreadsheet software to assist with analysis. For example, you can export your Paperform survey data to Excel or Google Sheets to perform valuable calculations like:
From here, it’s easy to filter your results, test your hypotheses and display your quantitative data as charts or graphs.
The output of qualitative data is usually displayed in the form of a report, whitepaper, or thought leadership content. Expect plenty of quotes, descriptions, explanations, and even images to support your research.
The following techniques will help you turn mountains of text-based data into meaningful output.
Whichever analysis you choose, qualitative data can be painstaking to sift through. Luckily, integration platforms like Zapier or Make can make it easier.
You can integrate Paperform with Monkeylearn, an AI-powered qualitative data and visualisation tool, for example. Monkeylearn will automatically classify the survey text and scan for keywords when a new Paperform submission is received.
Still wondering whether your survey is qualitative or quantitative? The answer is it depends. If you ask rating, scale, or close-ended questions where your participants provide answers that fall into categories, then you have a quantitative survey.
If you use text-based or open-ended questions about people’s experiences, and the answers are non-numerical, you’ve got yourself a qualitative survey.
No matter what kind of survey you’re making, Paperform can help you do it. With 650+ templates and question types suitable for either qualitative or quantitative research, we’ve got you covered.
Paperform has the perfect balance of beauty and brains. That means you can create a beautiful, branded survey with all the advanced features you need under the hood, including conditional logic, advanced calculations, and robust built-in data analytics.
And with direct and Zapier integrations to 3,000+ apps, it’s a cinch to analyse your survey results across platforms and share your insights with your team. Ready to research? Try Paperform today with a 14-day free trial—no credit card required.
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