Empirical research is both qualitative and quantitative. The term “empirical” doesn’t refer to a specific type of data or analysis. It refers to any research based on direct observation or experience rather than pure theory. A lab experiment measuring blood pressure changes is empirical. So is a series of in-depth interviews with cancer survivors. Both gather firsthand evidence from the real world, which is what makes research empirical.
What “Empirical” Actually Means
The core idea is straightforward: empirical research derives knowledge from actual experience rather than from theory or belief. The American Psychological Association defines the empirical method as any procedure that relies on experimentation and systematic observation rather than theoretical speculation. The key distinction isn’t between numbers and words. It’s between going out and collecting original data versus analyzing existing ideas from your desk.
A theoretical paper, by contrast, synthesizes and reinterprets findings from other studies to build or refine a conceptual framework. It doesn’t generate new observations. Empirical research does. Every empirical study starts with a research question, designs a specific plan to answer it, then gathers and analyzes primary data. Whether that data comes in the form of spreadsheets or interview transcripts is a separate decision entirely.
How Quantitative Research Fits
Quantitative empirical research collects numerical data and uses statistical analysis to draw conclusions. Common designs include large-sample surveys, controlled experiments, and coded datasets. The researcher typically aims for a large enough sample size that statistical tests can demonstrate whether results are significant, meaning unlikely to have occurred by chance. The researcher’s role is generally neutral and noninteractive, and findings are often presented through graphs, charts, or tables.
Examples include a clinical trial tracking recovery times across two treatment groups, a national survey measuring attitudes toward a policy, or a study coding thousands of court cases to identify sentencing patterns. All of these are empirical because they collect original, observable data. They’re quantitative because that data is numerical.
How Qualitative Research Fits
Qualitative empirical research gathers non-numerical data to develop a deeper understanding of behavior, experience, or meaning. Methods include interviews, ethnographic observation, focus groups, textual analysis, and case studies. The data might be a transcript of a conversation, field notes from observing a community, or a detailed profile of a single patient’s experience over time.
Validity in qualitative work doesn’t depend on how many participants you recruit. It depends on the depth and richness of the information generated. A qualitative approach leaves open the opportunity for unexpected responses and lets meaning emerge from the data rather than testing a predetermined hypothesis. Participant observation, for instance, is widely recognized in health and medicine as a tool that emphasizes the situated and the empirical. Researchers can draw on many types of materials, from recorded conversations to personal diaries, to generate empirical evidence.
The confusion often arises because people associate “empirical” with numbers. But a researcher sitting in a hospital ward for six months, systematically recording how nurses communicate with patients, is doing empirical work. The observations are direct, planned, and documented. They just aren’t counted.
Mixed Methods: Using Both at Once
Many empirical studies combine qualitative and quantitative data in what’s called a mixed methods design. There are three basic structures. In an exploratory sequential design, the researcher first collects qualitative data (such as interviews), then uses those findings to shape a follow-up quantitative phase (such as a survey). An explanatory sequential design reverses the order: quantitative data comes first, and qualitative data collection follows to help explain or contextualize the numbers. In a convergent design, both types of data are collected and analyzed during the same timeframe, then compared side by side.
Integration can happen at multiple levels. Researchers might connect the two datasets through a shared sampling frame, build one data collection approach based on results from the other, or merge both databases for joint analysis. At the reporting stage, some researchers transform one type of data into the other, converting interview themes into numerical codes, for example, so the two can be directly compared. Others use joint displays, visual tools that place qualitative and quantitative findings together to reveal insights that neither dataset would show alone.
The existence of mixed methods research is itself strong evidence that “empirical” is not synonymous with either qualitative or quantitative. It’s the umbrella that holds both.
How to Tell If a Study Is Empirical
Rather than looking at whether a study uses numbers or narratives, look for three things: a specific research question, a planned design for answering it, and the collection of original data. If a paper gathers new observations from the world, whether through a controlled experiment, a survey, a set of interviews, or months of fieldwork, it’s empirical. If it only reviews, reanalyzes, or theorizes about other people’s findings, it isn’t.
This matters when you’re evaluating sources for a class assignment, a literature review, or your own understanding of a topic. A professor asking for “empirical sources” is asking for studies that collected firsthand data. You can cite a randomized controlled trial, a qualitative interview study, or a mixed methods project. All qualify. An opinion essay or a theoretical framework paper would not, regardless of how many numbers it references from other studies.