Once you understand what research is, the natural next question is: what kind are you doing? "Research" is an umbrella term. Underneath it sit several distinct types, classified along different dimensions — by purpose, by the nature of the data, and by how far the question has been explored before. Knowing which type you are doing tells you what methods to use, what counts as a good answer, and how to write it up.
This guide maps the main classifications of research and shows how they overlap, with concrete examples for each.
Classifying research by purpose: basic vs applied
The oldest and most fundamental split is between research done to know and research done to do.
Basic (pure / fundamental) research
Basic research seeks knowledge for its own sake. It aims to expand understanding of how the world works without an immediate practical application in mind. A physicist studying the behavior of subatomic particles, or a psychologist mapping how memory consolidates during sleep, is doing basic research. The payoff may be enormous — but it is not the point, and it may be decades away.
Applied research
Applied research is aimed at solving a specific, practical problem. A team testing whether a new mosquito net coating reduces malaria transmission in a particular district is doing applied research. The question is concrete, the stakes are immediate, and success is measured by whether the problem moves.
The two are not rivals. Applied research constantly draws on the stockpile of knowledge that basic research builds, and applied findings often raise new fundamental questions. Most real projects sit somewhere on the spectrum rather than at one pole.
Classifying research by data: qualitative vs quantitative
A second, equally important dimension is the kind of evidence you collect.
- Quantitative research deals in numbers — measurements, counts, scores — and uses statistics to test relationships. "Does class size affect exam performance?" answered with test scores across hundreds of students is quantitative.
- Qualitative research deals in meaning — words, observations, experiences — and seeks to understand how and why. "How do students experience large classes?" answered through interviews is qualitative.
- Mixed-methods research deliberately combines the two to get both the scale of numbers and the depth of words.
This distinction is so central to designing a study that we cover it in depth in our guide to qualitative vs quantitative research. For now, the key point is that it is a different axis from basic-versus-applied: a study can be applied and quantitative, basic and qualitative, or any other combination.
Classifying research by depth: exploratory, descriptive, explanatory
A third way to classify research is by how much is already known about the question — which shapes how ambitious your aims can realistically be.
Exploratory research
When a topic is new or poorly understood, exploratory research maps the terrain. It does not try to give final answers; it tries to clarify the question, surface variables, and generate hypotheses for later work. Early interviews with users of a brand-new technology are exploratory.
Descriptive research
Descriptive research answers what is happening — it characterizes a population, situation, or phenomenon accurately without trying to explain causes. A national survey reporting the prevalence of hypertension by age group is descriptive.
Explanatory (causal) research
Explanatory research asks why — it tests cause-and-effect relationships between variables. A controlled trial showing that a specific intervention causes a drop in hypertension is explanatory. This type makes the strongest claims and therefore demands the most rigorous design.
These three often form a natural progression: you explore a new area, describe what you find, and eventually explain the mechanisms. Each maps onto particular research designs, which is where this classification becomes practical.
Other useful distinctions
A few more labels you will meet:
- Experimental vs non-experimental. In experimental research you manipulate a variable and control conditions; in non-experimental (observational) research you measure things as they are.
- Cross-sectional vs longitudinal. Cross-sectional research takes a snapshot at one point in time; longitudinal research follows the same subjects over months or years.
- Primary vs secondary. Primary research collects new data first-hand; secondary research analyses data or sources someone else produced.
None of these labels are mutually exclusive. A real study might be applied, quantitative, explanatory, experimental, and longitudinal all at once. The labels are tools for thinking clearly about what you are doing — not boxes you must fit into.
How to choose the right type for your study
You do not pick a "type" off a menu. The type follows from your question:
- Start from the question. Is it asking what exists, how something is experienced, or whether one thing causes another? That tells you exploratory/descriptive/explanatory.
- Decide what evidence would answer it. Numbers, words, or both? That sets qualitative/quantitative.
- Check the stakes. Are you building general knowledge or solving a concrete problem? That places you on the basic–applied spectrum.
Once you can name the type of research you are doing, the methodology decisions that follow — design, sampling, analysis — become far easier, because each type has well-established methods to draw on.
Next steps
Understanding types of research is the bridge between the broad idea of what research is and the practical work of designing a study. When you are ready to turn your question into a concrete plan — choosing a design, a sample, and an analysis strategy — move on to our guide on research methodology.
And when you start actually writing — framing your question, structuring your argument, and citing real sources as you go — that is exactly what PaceResearcher is built for.