Qualitative vs. Quantitative Research

When people imagine "research," they usually picture numbers, a stereotype that aligns with quantitative research: anything measured numerically, like survey ratings, cell counts, reaction times, or growth rates over time.

But a huge amount of rigorous science is qualitative: descriptive, non-numerical data that captures experiences, meanings, or themes, often gathered through interviews, open-ended survey responses, focus groups, or careful observation written up as detailed notes. While quantitative research is generally better at answering "how much" or "how many," qualitative research is generally better at answering "why" or "how does this actually feel from the inside."

Qualitative research has its own rigor, too, even without statistics attached. Researchers doing interview-based work often use a process called coding, where they systematically read through transcripts and tag recurring themes, then check whether multiple researchers independently identify similar patterns. This builds trustworthiness into the analysis that doesn't rely on a p-value at all. This process, sometimes called thematic analysis, can be just as time-consuming and methodical as running statistics on a spreadsheet.

A third term worth knowing is triangulation, which is what researchers call it when they use multiple methods (ex. a survey, a set of interviews, and direct observation) to check whether they all point toward the same conclusion. If your quantitative data says students are stressed about deadlines and your qualitative interviews independently confirm the same theme in students' own words, that agreement across methods makes your overall finding considerably more convincing than either piece of evidence would be alone.

If you're interested in public health, psychology, sociology, or education research, don't assume you need a data set full of numbers to be doing "real" science. If you're designing your own project and aren't sure which approach fits, a useful starting question is simply: am I trying to measure something, or am I trying to understand something? Measuring calls for a quantitative design; understanding usually calls for a qualitative one, or a mix of both.