Explanatory Research: Definition & Example
Descriptive research tells you what is happening. Exploratory research helps you figure out what might be happening. But when you need to understand why something is happening — when you need to identify causes, mechanisms, or explanations — you turn to explanatory research. This type of research sits at the heart of scientific inquiry because it moves beyond surface-level observation to uncover the relationships and forces that drive a phenomenon. If your thesis question includes words like "why," "how does," or "what causes," you are almost certainly conducting explanatory research. In this article, you will learn exactly what explanatory research involves, how it differs from other approaches, and how to apply it effectively with the help of a detailed example.
What Is Explanatory Research?
Explanatory research is a type of study designed to explain why a phenomenon occurs by identifying causal relationships between variables. While descriptive research documents patterns and exploratory research generates initial ideas, explanatory research tests specific hypotheses about the reasons behind those patterns.
The hallmark of explanatory research is its focus on causation. The researcher begins with a clearly stated hypothesis — for instance, "Higher levels of job autonomy lead to greater employee satisfaction" — and then designs a study to test that hypothesis. The design might be experimental, quasi-experimental, or correlational with statistical controls, depending on the nature of the variables and practical constraints.
Explanatory research typically builds on earlier descriptive or exploratory work. You might first conduct a descriptive survey showing that employee satisfaction varies widely across departments. Then you might run exploratory interviews to identify possible reasons. Finally, you design an explanatory study to test whether a specific factor, such as job autonomy, actually accounts for the variation. This progression from description to exploration to explanation is one of the most common research trajectories in the social sciences.
When Should You Use Explanatory Research?
Explanatory research is appropriate when the phenomenon you are studying is already well described and you have a reasonable theoretical basis for proposing a causal mechanism. If the literature already documents that employee turnover is high in a particular industry, but nobody has convincingly explained why, an explanatory study is the logical next step.
You should also consider explanatory research when your thesis requires you to go beyond reporting data. Many supervisors expect master's-level work to include an explanatory component, because demonstrating that you can reason about causes is a key marker of academic maturity. However, explanatory research demands more rigorous design than descriptive work. You need to control for confounding variables, justify your causal model, and use statistical techniques that can distinguish genuine effects from spurious associations.
It is worth noting that explanatory research does not always require a laboratory experiment. Regression analysis, structural equation modelling, and difference-in-differences designs can all support causal inference in observational data, provided the researcher addresses potential biases transparently.
Suppose earlier research has described that completion rates for online university courses range from 5 % to 85 %, and exploratory interviews suggest that course structure might play a role. You hypothesise that courses with weekly deadlines have significantly higher completion rates than courses that allow students to work at their own pace. To test this, you collect data from 120 online courses at the same university, record whether each course uses weekly deadlines or a self-paced format, and obtain the completion rate for each course. After controlling for course difficulty, student demographics, and instructor experience using multiple regression, you find that the deadline variable explains a statistically significant portion of the variance in completion rates. This result supports your causal hypothesis and moves the conversation beyond mere description.
Strengths and Limitations
The primary strength of explanatory research is depth. By testing specific hypotheses, you generate knowledge that can inform policy, practice, and further theory. Knowing that weekly deadlines improve course completion is far more actionable than simply knowing that completion rates vary.
However, explanatory research has important limitations. Establishing true causation is difficult outside a randomised experiment. Even with statistical controls, observational explanatory studies can suffer from omitted-variable bias — the possibility that an unmeasured variable is actually responsible for the effect you observe. Transparency about these limitations is not a weakness; it is a sign of good scholarship.
Another challenge is the requirement for a solid theoretical framework. If you propose a causal mechanism without grounding it in existing theory, reviewers will question why that particular explanation should be taken seriously. Always connect your hypothesis to established models, previous findings, or logical reasoning before you collect data.
Conclusion
Explanatory research is where science truly begins to deliver answers. By moving beyond description and exploration, it tackles the "why" questions that matter most to decision-makers, practitioners, and fellow researchers. If your thesis aims to explain a phenomenon, invest time in building a strong theoretical foundation, formulating a clear hypothesis, and choosing a design that allows you to control for alternative explanations. The extra effort pays off in the form of findings that do more than describe — they illuminate.
