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, how you can turn one into the other, and when you might use which method.
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 vary using unstructured or semi-structured techniques. Common methods include focus groups, individual interviews, observation or immersion, and diary studies. The sample size is typically small, and respondents are selected to fulfill a given quota.
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 are much more structured; they include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.
In terms of the actual data, here are some of the key differences:
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% of vehicles are cars, 30% are trucks, and the rest are motorbikes. This would be a quantitative fact. 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.
Let’s consider a bunch of email conversations. In its raw form, this would be considered qualitative data. To answer the 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.
Let’s consider an example. If you were to measure user behaviour on a website, you might learn that 25% 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 behaviours for the better.
However, this data doesn’t tell you why people did what they did.
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.
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.
In your research, consider using both qualitative and quantitative methods together to be better equipped to solve the problem at hand.
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