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If you're trying to collect data for your business, it's important to know the differences between qualitative and quantitative data. Understanding the purpose of both types of research can help you create a better respondant experience and, in turn, uncover more relevant insights.
In this blog post we'll explore how to collect qualitative and quantitative data, the questions commonly used to collect them, and how to combine qualitative and quantitative data types to power up your insights.
What is Quantitative research and data?
Simply put, quantitative research seeks to quantify, count, or measure. It typically seeks to answer questions such as how many, how often, and to what extent. It's typically analyzed using statistical methods and presented in tables, graphs, percentages, or other statistical representations.
What could it look like to apply quantitative research methods? Let's pretend for a moment that we are an online retailer for refrigerators. When someone visits our site and goes to close the webpage, an exit survey pops up for them. We ask them how long they've been considering making a purchase, where they fall on a scale from 1–10 on their purchasing journey, the size of a refrigerator they're looking to purchase, and how many additional retailers they're considering. The answers to all of these questions can be answered with quantitative data.
What is qualitative research and data?
Qualitative data is essentially anything that describes or explains. From observations of an interaction, to quotes from people about their experiences, attitudes, beliefs, and thoughts, these are all examples of qualitative data. It can also be represented in words, images, video, audio, transcripts, and more.
While qualitative research can be more strenuous to quantify and dissect, it offers the respondant more freedom to express themselves. If you want to truly understand intent, opinion, and experience, you'll want to integrate qualitative data into your toolkit. Let's go back to the example of the refrigerator retailer. You might include a survey at the end of the checkout process which asks open-ended questions. You could ask why the customer selected the fridge that they did, how they heard about you, and what improvements they'd like to see in your website. The benefit here to using less rigid methods of collection is that you get to hear the true voice of the customer.
Qualitative vs. Quantitative research: What is the difference?
Both types of research differ in their objectives, the method of data collection, and the data they produce. Quantitative data collection is rigorous and more reliable. The data is more easily compared and can be tested for significance (more on this later). Qualitative methods, on the other hand, are less formal and more flexible, and offer you greater insight into a personal experience.
Okay, which is better?
Sorry, it's a trick question. We're not making them compete for the MVP. Instead, they work together to provide powerful, guiding insight.
"The advantage of a mixed methodology is you get both projectable or predictive data and explanatory or contextual insights," says Christine Shimoda, a Market Research Strategist with 20 years of experience. "Using a quantitative methodology, a company could confidently learn that among its target audience, 85% are likely to buy X product within the next year, and that men are more likely than women to buy said product. A qualitative methodology would allow that company to understand why men are more likely than women to buy the product."
Quantitative research provides evidence and predictions. Whereas qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need to answer.
Qualitative research methods
Some frequently used qualitative research methods include In-depth interviews, Focus groups, Ethnography, and User Testing.
Quantitative research methods
Common quantitative research methods include Surveys, Structured Observations, Experiments, and Customer Reviews.
Data analysis
"Without analysis, data is just numbers or anecdotes," says Shimoda. "The analysis is what brings the meaning of the data to the surface. It's what identifies the trends, story, and insights. It translates data from something that is merely interested to something that is useful and actionable."
A technique called coding is used to organize substantial amounts of qualitative data into bite-sized chunks. Good old data analysis is the process of turning raw numbers (quantitative data) into meaningful information. This is commonly done using techniques such as frequency tables and proportions.
So I ran a survey, now what?
Before grasping an insight and running to make sweeping changes to your website ( or product) we encourage you to take a second and slow down first. Confirming whether you have good data is the first step to deciding how much weight to put in your findings.
A few signs your data may not be "good":
respondants sped through the quiz (are there folks that took 10 seconds to finish while the average time to complete is closer to 2 minutes?)
are there respondants who selected the same option across all questions (always selected option "b")
non-sense responses (asdfjk isn't a proper open-ended response, so, maybe don't include this user in data processing)
What to do once you've quality-checked your data
If you're trying to validate any sort of hypothesis, you may want to consider checking for statistical significance. We know it's probably been a minute since you've been in a stats class; luckily, Harvard Business Review has put together a full refresher for you to dive into. Evaluating for statistical significance is a way to determine whether your results were purely chance, or if they're the result of actual interest.
Final thoughts
The key to successful qualitative and quantitative research is iteration. This doesn't mean doing the same thing again and again. Rather, it means continually returning to your questions, methods, and results to uncover new ideas and insights. You might spot a new pattern, form a new hypothesis, or walk away with a completely different understanding of previous results.
