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Your research question should begin with what you genuinely need to know, but its final form must respect what your evidence can actually answer. Available data may refine or constrain the question, but they should not silently redefine the problem.
Read Guide →When the most important research question cannot be answered directly, you do not necessarily need to abandon it. You may instead identify the strongest answerable question that genuinely contributes to it while remaining explicit about what remains unknown.
Read Guide →A research question should name a proposed mechanism when investigating that mechanism is genuinely part of the study. If the mechanism is only a plausible explanation for an outcome, building it into the question can prematurely assume what the research should test.
Read Guide →A research question can quietly treat an uncertain claim as though it were already established. Identifying these hidden assumptions helps prevent the study from beginning with the very conclusion it should be investigating.
Read Guide →Naming a population in a research question does not necessarily mean researchers can identify who belongs to it. A defensible study requires a population that can be defined operationally, connected to an accessible source of participants or cases, and matched to the conclusions the study intends to make.
Read Guide →Two groups can be easy to compare statistically while making little sense as a scientific comparison. A meaningful research question requires a comparator that helps answer the substantive question rather than merely providing a second group.
Read Guide →A study can collect useful data and still be incapable of producing the evidence its research question requires. Working backward from the intended answer reveals whether the proposed design can actually support the claim being asked for.
Read Guide →A research question can appear perfectly clear to its author while allowing another researcher to design a substantially different study around the same wording. Testing how others interpret the question can expose ambiguity before it spreads into the design, measurement, and analysis.
Read Guide →Before choosing a questionnaire, dataset, interview protocol, or experiment, you should be able to describe what evidence would actually answer the research question. That specification creates the bridge between the question and the study design.
Read Guide →A study should not need a statistically significant result in the expected direction to count as successful. Testing what you would regard as a successful outcome before collecting data can reveal whether the research question is genuinely open to evidence.
Read Guide →Two research questions can belong in the same study when they address a coherent research problem and can be answered through a compatible design, population, data collection strategy, and analytical plan. Sharing participants or data alone is not enough.
Read Guide →Two related research questions may still belong in separate studies when they pursue different scientific purposes, require substantially different designs, or cannot both be answered rigorously within one coherent project.
Read Guide →Several research questions can involve the same participants without constituting one study. Study boundaries depend primarily on the research questions and design, although consent, ethics, data use, and transparent reporting must also be considered.
Read Guide →A secondary question becomes distracting when it no longer supports the study's primary purpose and begins competing for conceptual, methodological, analytical, or interpretive attention.
Read Guide →Available data can inspire worthwhile research questions, but availability alone is not a sufficient reason to add one. The question still needs scientific value, suitable evidence, and an honest analytical rationale.
Read Guide →A collaborator's interest can reveal a valuable research question, but interest alone is not enough to justify adding it. The question should strengthen the study, fit its design, and be answerable without compromising its primary purpose.
Read Guide →Exploratory questions can be specified before data collection when they are scientifically worthwhile but not appropriate for strong confirmatory claims. Planning them can improve measurement and transparency without turning exploration into confirmation.
Read Guide →There is no universal maximum number of secondary research questions. A study can support only as many as it can answer coherently and rigorously without compromising its primary purpose, evidence, analysis, or feasibility.
Read Guide →A study becomes part of a research program when the scientific problem requires a coordinated sequence of distinct investigations rather than one design attempting to answer every important question at once.
Read Guide →A thesis can contain more than one research question when the questions address connected aspects of one research problem and can be answered rigorously within the scope, resources, and requirements of the degree.
Read Guide →A dissertation may be stronger as several linked studies when its major questions require different designs, populations, stages, or forms of evidence but still contribute to one coherent doctoral research problem.
Read Guide →A thesis or dissertation usually needs an appropriate contribution for its degree level, not a completely unprecedented topic. The required kind and degree of originality depend on your institution, discipline, program, and assessment criteria.
Read Guide →Testing an existing finding in a new population can make an original contribution when the population difference matters to the claim being tested. Simply changing participants, location, or demographic group does not automatically make a study original enough.
Read Guide →Using an established method does not prevent a study from being original. Applying it to a new problem can make a genuine contribution when the application answers an unresolved question, reveals new evidence, or requires a meaningful adaptation.
Read Guide →Using a new dataset can support original research, but the dataset's newness is not enough by itself. The stronger contribution comes from what the data allow you to test, estimate, discover, compare, or understand that existing evidence could not adequately establish.
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