Turn the question into a set of tasks
A feasible research question is one you can answer credibly with the time, access, skills and materials you actually have. Start by writing down what an answer would require, rather than judging the question by how interesting it sounds. If you ask, “How does remote work affect productivity?”, you might need to define productivity, identify a group of workers, choose a period and decide what evidence could show an effect. Each unanswered choice adds work.
Translate your question into a short task list: define key terms, find relevant evidence, assess its quality, analyze what it shows and communicate a conclusion. Then mark any task that depends on something you do not yet have, such as permission to survey employees or access to workplace records. This makes feasibility a practical check, not a guess about your motivation.
Check whether the evidence is reachable
Ask what evidence could answer the question and whether you can obtain it. A question about employees at a specific company may require private records or cooperation from staff. If neither is available, enthusiasm will not close the gap. A question about how different publications describe remote-work productivity may be answerable with accessible articles and reports, though it addresses claims about productivity rather than measuring productivity itself.
Do a small source test before committing. Search using several versions of your key terms, open a few promising items and note what they contain. Are they relevant to your population and period? Do they provide observations, methods or definitions you can inspect, or only opinions? Can you access enough material to compare findings? If the evidence is thin, consider changing the population, outcome or type of answer you seek. Keep the wording of the question aligned with what the available evidence can support.
Make a realistic time and resource budget
Estimate the work in stages rather than assigning one deadline to the entire project. Allow time to locate sources, read and take notes, resolve gaps, analyze evidence, draft and revise. Include a buffer for sources that turn out to be unsuitable. If you have two weeks and only a few hours each week, a question requiring interviews, transcription and comparison of responses may be difficult to complete well. A focused review of accessible documents may be more realistic.
Check resources beyond time. Do you need paid materials, specialized software, a particular language skill or help interpreting a dataset? Can you use a simpler method without losing the point of the question? For example, instead of trying to calculate productivity changes across an entire industry, you might compare how three accessible studies define and measure productivity. That narrower project still requires careful reading, but it removes the need to collect data you cannot reach.
Match the question to a manageable answer
Some questions are too broad because they invite more than one kind of answer. “How does remote work affect productivity?” could call for a causal estimate across many jobs and settings. A manageable version might ask, “How do selected studies of office workers define productivity when examining remote work?” The revised question specifies what you will examine and avoids promising a conclusion about every worker. It also gives you a clear way to decide which sources belong in the project.
Narrowing is not always the answer. A question can also be so narrow that no useful evidence exists or the result is trivial. Aim for a scope that leaves room to compare evidence and explain a meaningful finding. Write a one-sentence description of your likely final answer. If that sentence would require claims you cannot verify, revise the question. If you can already answer it without examining any evidence, ask what uncertainty the project would genuinely investigate.
Run a pilot and set a decision point
Before settling on the question, try one small piece of the proposed work. Read two or three candidate sources closely, test a search strategy or attempt the first step of your analysis. Record what worked, what was missing and how long it took. A pilot can reveal problems that a task list cannot: inconsistent definitions, inaccessible documents or data that do not contain the variable you hoped to study.
Decide in advance when to continue, revise or stop. You might continue if you can identify suitable evidence and a method for comparing it within your remaining time. Revise if the evidence supports a narrower question, and change direction if a necessary source or permission is unavailable. Keep a brief note explaining your decision. Feasibility is not a judgment about whether a topic matters; it is a way to choose a question you can answer honestly and well.
