Research Gap, Problem Statement and Research Question, Explained with an Example

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Below, we explain how the three connect, and show how a real gap can be spotted using ScholarsColab’s own data.

Research gap, problem statement and research question at a glance

Example: spotting a research gap in ScholarsColab’s data

To explain how a gap leads to a research question, we use ScholarsColab’s own data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Look at what is already known. In our data, review articles are cited more than original research overall:

Step 2
Look for what does not fit. The advantage is not the same everywhere: The review advantage is not the same everywhere: in Physical sciences & mathematics reviews receive about 3.7× the citations of original research, while in Management, economics & finance reviews are cited about as often as original research.

Step 3
Name the gap. Our data shows that the review advantage differs between fields, but not why. That unexplained difference is a research gap.

Step 4
State the problem. “Researchers and funders invest heavily in reviews, but it is unclear why reviews are far more influential in some fields than others, which makes it hard to know where reviews are most worth the effort.”

Step 5
Formulate the research question. “Why do review articles receive a larger citation advantage in the physical and life sciences than in management and economics?”

How to find a research gap

1. Read recent reviews in your area; their “future research” sections often name gaps directly.
2. Look for contradictions between studies, which signal an open question.
3. Look for under-studied settings: a population, region, method or time period not yet covered.
4. Check that the gap matters: who would benefit if it were filled?
5. Check feasibility: can you answer it with the time, data and skills you have?

What makes a good research question

• Focused: narrow enough to answer in one study.
• Clear: names the variables, population and setting.
• Researchable: can be answered with data you can collect.
• Relevant: addresses a real gap and a real problem.
• Original: adds something new, even if small.

Common mistakes reviewers point out

• A topic instead of a question, such as “machine learning in healthcare”.
• A gap claimed without evidence from the literature.
• Questions too broad to answer in one study.
• A mismatch between the question, the objectives and the methods.

Frequently asked questions

Use this data in your own work

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