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When you collect data affects what relationships, changes, and temporal patterns your study can observe. The number, sequence, and spacing of measurements should follow the process your research question is trying to understand.
Read Guide →Using interviews and observations, or several other data collection techniques, does not automatically make a study mixed methods. What matters is the methodological nature of the evidence and how the different components are designed, analyzed, and related.
Read Guide →More sources, methods, or measures can strengthen a study when they address different limitations or provide genuinely useful evidence. Simply adding more, however, does not make weak evidence strong.
Read Guide →A data collection method may fit your research question conceptually but still be inappropriate if it places unreasonable demands on participants or cannot be implemented well. Learn how to balance evidence quality with burden and feasibility.
Read Guide →Sometimes the evidence that would answer your question most directly cannot realistically or ethically be collected. The solution is not to pretend a convenient substitute is equivalent, but to find the best defensible alternative and adjust the question or claim when necessary.
Read Guide →A diverse sample can help researchers study variation that a narrower sample might miss, but diversity is not automatically the same as representativeness. What matters is which differences are relevant to the research question and the conclusions being drawn.
Read Guide →Representation asks whether relevant people or groups are present in research. Representativeness asks how adequately the sample reflects a defined target population for the inference being made.
Read Guide →Sex and gender can both matter in research, but they do not necessarily represent the same constructs. Distinguishing them helps researchers measure the variable they actually need and interpret observed differences more carefully.
Read Guide →Analyzing results by sex or gender can reveal important differences that an overall average conceals, but not every dataset supports a meaningful subgroup comparison. The decision should follow from the research question, study design, sample size, and relevant scientific or reporting requirements.
Read Guide →Research participation becomes more accessible when researchers examine what the study requires of participants and remove barriers that are not necessary to answer the research question. Accessibility can involve communication, disability accommodations, scheduling, location, technology, language, and participant burden.
Read Guide →Adding participants from more groups can broaden whose experiences appear in a study, but diversity alone does not establish representativeness. Representativeness depends on the target population, selection process, and inference researchers want to make.
Read Guide →Inclusive research does not mean including everyone. Researchers need a population broad enough to capture relevant variation but sufficiently defined to answer the research question safely and meaningfully.
Read Guide →You should usually know how your data will answer your research questions before collecting it. Planning the analysis early can expose design problems, clarify what data you actually need, and reduce data-driven analytical decisions.
Read Guide →A useful analysis plan does more than name a statistical test. Before data collection, it should connect each research question to the data, comparisons, analytical methods, assumptions, and decisions needed to answer it.
Read Guide →An analysis is appropriate only when it answers the question your study actually asks using evidence your design can legitimately provide. Learn how to test that alignment before collecting data.
Read Guide →Every testable hypothesis should have a corresponding analytical path, but the relationship is not always one hypothesis to one statistical test. The analysis must evaluate the specific claim the hypothesis makes.
Read Guide →Your research question and inferential goal should drive the study design, not a favorite statistical test. Analysis still belongs in the design process because thinking ahead about statistics can reveal what data the study must collect.
Read Guide →You should usually determine your main analytical approach before collecting data, and often the primary statistical method as well. But good planning does not require pretending that every analytical decision can be made before you see the data.
Read Guide →Collecting data without knowing how to analyze them does not automatically ruin a study, but it can reveal serious mismatches among the research question, design, measurements, and analysis. The solution is to diagnose the problem before choosing a convenient statistical test.
Read Guide →Qualitative studies benefit from planning analysis before data collection, but the plan should not predetermine what the data must reveal. Good qualitative planning establishes an analytical direction while preserving the flexibility required by the methodology.
Read Guide →A mixed-methods study needs more than separate quantitative and qualitative analyses. Planning integration before data collection helps ensure the two strands can actually be combined to answer a question that neither would address as fully alone.
Read Guide →An analysis plan does not need to predict every feature of data that do not yet exist. The important distinction is between decisions that should be made before results are known and flexibility that is genuinely required by the data, methodology, or research purpose.
Read Guide →A statistician or methodologist can often contribute most before data collection begins, when the research question, design, measurements, sampling, sample size, and analysis can still be changed.
Read Guide →Pilot and feasibility studies both reduce uncertainty before a larger study, but the terms are not simply interchangeable. Feasibility is the broader question of whether and how a future study can be done, while a pilot study typically tests part or all of the intended study on a smaller scale.
Read Guide →Not every research project needs a formal pilot or feasibility study. Preliminary work is most valuable when important uncertainties about whether or how the main study can be conducted cannot be resolved adequately from existing evidence or simpler testing.
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