Designing a Study You Can Defend
You can sometimes change a research design after data collection has started, but the timing, reason, consequences, and documentation of the change matter. The key is to adapt transparently without quietly redefining the study around what the emerging data appear to show.
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Designing a Study You Can Defend
Stopping or redesigning a study may be more defensible than continuing when the design can no longer generate evidence capable of answering a worthwhile research question. The decision should follow a structured assessment of what failed, what can be repaired, what evidence remains usable, and whether continued research is still scientifically and ethically justified.
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Doing Research Without Compromising Ethics
Research design is not separate from research ethics. An ethics committee needs to understand how a study is designed because poor or inappropriate methods can expose participants to burdens without a reasonable prospect of producing useful knowledge.
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Research Concepts, Terminology & Commonly Confused Ideas
Research design is the structure and logic of a study, while research methods are the procedures used to generate and analyze evidence within that structure. Understanding the distinction helps you explain not only what you did, but why those procedures can answer your research question.
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Artificial Intelligence in Research
Generative AI can help identify threats to research validity before data collection, including selection differences, measurement problems, attrition, and inappropriate causal assumptions. Learn how to evaluate AI-identified concerns and determine whether proposed safeguards actually address them.
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Artificial Intelligence in Research
Generative AI can help researchers evaluate whether a proposed study design is practical by examining recruitment, resources, timelines, expertise, ethics, and implementation requirements. Learn how to distinguish methodological suitability from operational feasibility and avoid relying on unsupported AI assumptions.
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Artificial Intelligence in Research
Generative AI can support research design planning, but decisions involving scientific purpose, causal assumptions, ethical trade-offs, methodological interpretation, and accountability must remain under human responsibility. Learn where AI assistance is useful and where researchers must exercise independent judgment.
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Finding What the Literature Really Says
A sophisticated analysis is not necessarily an appropriate one. Check whether the analysis respects how the data were generated, the outcome being studied, dependencies among observations, timing, comparisons, and the type of conclusion the researchers want to draw.
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Designing a Study You Can Defend
You should aim for the strongest design that can answer your research question and that you can actually execute well. When the ideal design is infeasible, the solution may be to choose a defensible alternative or revise the question, not to pretend that a weaker design answers the original question equally well.
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Designing a Study You Can Defend
Choosing a research design before identifying the evidence your question requires can produce a methodologically sound study that answers the wrong question. Learn how to work from the intended claim to the necessary evidence before committing to a design.
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Designing a Study You Can Defend
The strongest research design is not necessarily the most appropriate one. Understand what methodological strength means, why it depends on the question, and how to choose a rigorous design without sacrificing relevance, ethics, or feasibility.
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Designing a Study You Can Defend
A sophisticated research design is not necessarily more defensible than a simpler alternative. Learn when methodological complexity improves evidence, when it introduces avoidable weaknesses, and how to justify a design that is no more complicated than the research question requires.
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Designing a Study You Can Defend
A study can use an established research design correctly and still fail to answer the question that motivated it. Learn how to identify mismatches between research purpose, evidence, methodology, and conclusions before they undermine your study.
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Designing a Study You Can Defend
A research design does not become appropriate simply because it is widely used in your discipline. Learn how to distinguish established methodological conventions from the evidence your question actually requires, and when departing from familiar approaches is justified.
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Designing a Study You Can Defend
Words such as effect, impact, influence, and determine may imply causation even when a study is designed only to describe, associate, or predict. Learn when to avoid causal language, when observational research may legitimately pursue causal questions, and how to align research wording with the evidence a design can support.
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Designing a Study You Can Defend
Intervention fidelity concerns whether a research intervention is implemented as intended, but fidelity involves more than following a protocol exactly. Learn how to define essential components, distinguish fidelity from adherence and adaptation, and build a defensible fidelity plan before data collection begins.
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Designing a Study You Can Defend
Statistical power describes how likely a planned hypothesis test is to detect a specified effect under particular assumptions. Learn what 80% and 90% power mean, how effect size and sample size influence power, why underpowered studies can produce misleading interpretations, and how to use power analysis responsibly when designing research.
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Designing a Study You Can Defend
When the entire eligible population is small, conventional sample size targets may be impossible to achieve. Learn how to define the population, distinguish descriptive from causal objectives, select appropriate designs, and make defensible conclusions without treating a small population as an automatic methodological weakness.
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Designing a Study You Can Defend
Stakeholders can contribute to study design, recruitment, measurement, data-collection procedures, interventions, and sometimes data collection itself. Their involvement can improve relevance and feasibility, but methodological and ethical requirements still apply.
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Designing a Study You Can Defend
Stakeholder disagreement is not necessarily a failure of engagement. Researchers should make differences explicit, identify what is driving them, use agreed decision processes, preserve methodological and ethical requirements, and document how consequential decisions are reached.
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Designing a Study You Can Defend
Unreliable connectivity and electricity can interrupt data collection, exclude participants, damage records, and create systematic missingness. Learn how to design offline-capable research procedures, establish secure backup and recovery arrangements, and determine when infrastructure limitations require changes to the research question.
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Designing a Study You Can Defend
Some research designs depend on specialists who are unavailable, overcommitted, or geographically inaccessible. Learn how to identify essential specialist responsibilities, distinguish tasks that can be delegated from those requiring professional expertise, and develop alternative procedures without compromising research validity or participant safety.
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Designing a Study You Can Defend
Long travel distances can discourage participation, increase attrition, and systematically exclude people living far from research facilities. Learn how to reduce travel burden through appropriate site selection, remote procedures, flexible scheduling, and defensible measurement adaptations without compromising the research question.
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Designing a Study You Can Defend
The strongest research design is not necessarily the most complex one. Choose the simplest design that can answer the research question credibly, and add complexity only when it produces information or protection against bias that matters to the intended inference.
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Designing a Study You Can Defend
Changing a research design is not inherently poor methodology. Adaptation may be defensible when it responds to a genuine problem while preserving a credible connection between the research question, the evidence being generated, and the conclusions the study will support.
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