Review questions
Integrated summary: From Research Problem to Question and Literature Map
Research methodology begins before choosing a data-collection tool. The first task is to turn a broad topic into a defined problem: a clear context, an observable phenomenon, a gap in knowledge and a reason the gap matters. A preferred explanation should not be embedded in the problem as fact. If all we know is that completion falls, we state that and investigate factors or explanations rather than deciding in advance that motivation or video length is the cause. This preserves three levels of claim: describing what happens, estimating an association between measured features, and claiming that changing one factor causes a change in an outcome. Each level requires evidence and a design that can support it.
The research question then becomes the center of the plan. The question states what we want to know, the objective converts it into a research action, a hypothesis—when appropriate—states a testable expected pattern, and variables identify what will be measured. These are not independent boxes in a template; they must align. A question about the relationship between waiting time and satisfaction justifies an objective to estimate that relationship and measure the two variables, but it does not automatically justify collecting dozens of unrelated variables or writing a causal conclusion. A construct such as motivation also needs an operational definition before it becomes an implementable measurement.
Literature helps test and refine this plan. Structured searching begins with the concepts in the question, synonyms for each concept, suitable sources and a documented strategy. OR is commonly used to combine alternative terms within a concept and AND to connect different concepts; the strategy is then tested and revisions are recorded. When the task is a systematic review, planning, documentation and study-selection requirements become more formal, so an ordinary literature review should not be labelled “systematic” merely because several search terms were used. After retrieval, sources are appraised for relevance and evidential role, then moved from separate summaries into a synthesis that shows where findings converge, where they differ, and whether differences may arise from populations, measurements or designs.
Finally, conceptual framing and an alignment matrix turn these ideas into a testable plan. Important constructs, expected relationships and plausible alternatives are made visible without treating arrows as proven causes. We then inspect the chain: problem → question → objective → evidence → design → permissible inference. If the question concerns participants’ experiences, digital logs alone are insufficient; if the question asks for a proportion, a small interview set cannot estimate it; and if the question is causal while the plan is a simple observational survey, either the claim must be narrowed to association or a stronger design must be planned. This alignment provides the bridge to the next subject, where quantitative, qualitative and mixed-methods designs and their inference limits are compared.
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