Qualitative vs. quantitative data: which do you need?
There are two key types of data – qualitative and quantitative. But which do you need to use for your research?
The choice between qualitative vs. quantitative data depends on the topic and format of your study. You’ll also need to think about the kind of data you’d like to gather.
In this post, we’ll look at the difference between these two data types, and the pros and cons of each. We’ll also explain how to choose the right option for your research.
Qualitative data is non-numerical. For example, it could be:
This data tends to be more open-ended. It’s a good option for exploratory research, as you can use it to identify connections between data.
Want to learn more about how your participants think and feel? Qualitative data is the best choice. You can also use it to analyze your topic in wider social contexts.
There are many different ways to get qualitative data. These include:
You can also gather it by analyzing other forms of non-numerical data, like photographs and videos.
Quantitative data is numerical data. You can typically collect it on a much larger scale than qualitative data. This means it can reflect a more diverse range of perspectives, making it ideal for confirmatory research and hypothesis testing.
Do you want to explore a specific hypothesis through your research? If so, then quantitative data is the best option.
You can gather quantitative data through experiments, surveys, and other tests. These give you numerical or other categorical data that you can analyze using statistics.
Quantitative data limits the types of responses that participants can provide. For example, you might use a Likert scale, where the participant submits how strongly they feel about a statement – from “strongly agree” to “strongly disagree”. With this scale alone, they can only give limited information. This restricts the depth of their responses.
When it comes down to qualitative vs. quantitative data, your research question will determine which is suited to your study.
Qualitative data is a great choice for developing a broader understanding of a topic. You can use it to gain insight from real-life experiences. Through this, you'll uncover new paths of inquiry for future research. For example, you could conduct interviews to find out about a person’s experience with a new piece of software. Without the constraints of a questionnaire, the participant can tell you about their experiences in detail. This way, you can learn about the context behind their answers.
But if there’s already extensive research on the topic, quantitative data is the best option. You can use it to test hypotheses. Quantitative studies are also generalizable - and you can replicate them. For example, if the qualitative data suggested that participants had negative experiences with the software, you could use a questionnaire to test this. This would allow you to confirm the hypothesis with a much larger group of people.
These two types of data can inform each other when used over multiple studies – much like with exploratory and confirmatory research. With qualitative data, you can’t test a hypothesis. But you can uncover potential hypotheses for future studies. You can then test these with quantitative data in a confirmatory study.
Whether you want to test a hypothesis or discover new insights, data is the beating heart of research. Qualitative and quantitative data need to be high-quality. Without this, researchers run the risk of replicating unconscious biases. They could even reduce the validity of their own studies.
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