Designing a Study You Can Defend
Clustering can substantially increase sample size requirements because participants within the same school, classroom, institution, or other group may provide correlated information. Learn how intraclass correlation, cluster size, the number of clusters, and the planned analysis influence statistical power and sample size, including why recruiting more participants from the same few clusters may be less useful than recruiting additional clusters.
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Designing a Study You Can Defend
A calculated survey sample size usually refers to the number of usable observations, not the number of people who must be invited. Learn how to adjust recruitment targets for expected nonresponse, distinguish response rates from participation yield, plan for subgroup differences, and understand why obtaining enough responses does not automatically eliminate nonresponse bias.
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Designing a Study You Can Defend
A statistically justified sample size may exceed the number of participants a researcher can realistically recruit. Learn how to evaluate whether the calculation is appropriate, distinguish recruitment limitations from methodological problems, improve study efficiency, and decide when a smaller exploratory or feasibility study is more defensible than an inadequately powered confirmatory investigation.
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Designing a Study You Can Defend
When the required sample size is unrealistic, several study elements may be reconsidered, but they are not equally interchangeable. Learn when to revise the research question, improve the design, reconsider outcome measurement, or reassess effect size assumptions, and how to distinguish scientifically justified revisions from changes made merely to produce a smaller calculation.
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Designing a Study You Can Defend
An effect size from a different population can inform sample-size planning, but it may not transfer reliably. Learn how to evaluate comparability, interpret population differences, and justify a defensible planning assumption.
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Designing a Study You Can Defend
The smallest effect size of interest identifies the minimum effect that matters for a research question. Learn how to establish a defensible threshold, distinguish it from statistical significance, and use it to plan an informative study.
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Designing a Study You Can Defend
Statistical significance may answer whether an effect differs from a null value without establishing its magnitude accurately. Learn when estimation precision is the more informative sample-size target, how to recognize research questions that require it, and how to plan accordingly.
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Designing a Study You Can Defend
Qualitative research generally does not require the statistical sample-size formulas used in quantitative studies, but it still requires a defensible justification for participant numbers. Learn when calculations are appropriate, why conventional formulas can be misleading, and how to explain sample adequacy in a research proposal or thesis.
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Designing a Study You Can Defend
Information power offers a methodological framework for judging qualitative interview sample adequacy through the relevance and richness of the data. Learn its five dimensions, how they interact, and how to use the framework without turning it into an arbitrary numerical formula.
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Designing a Study You Can Defend
A larger qualitative sample may expand the range of experiences represented, but excessive recruitment can strain analytical resources and limit engagement with individual accounts. Learn when a large sample is justified, when it becomes counterproductive, and how to balance breadth with interpretive depth.
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Designing a Study You Can Defend
Knowing when to stop recruiting qualitative participants requires more than reaching a target number or observing repeated responses. Learn how to make and document stopping decisions using methodology-appropriate adequacy criteria, participant variation, analytical needs, and ethical considerations.
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Designing a Study You Can Defend
A calculated sample size does not tell researchers how many people to approach or how long recruitment will take. Learn how to translate the required sample into a practical recruitment plan that accounts for eligibility, participation, attrition, study-site capacity, and realistic recruitment timelines.
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Designing a Study You Can Defend
A study requiring 300 usable participants may need substantially more than 300 invitations. Learn how eligibility, refusal, and attrition reduce recruitment yield, how to calculate appropriate contact and enrollment targets, and why the assumptions must reflect the actual research design.
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Designing a Study You Can Defend
Multi-site studies often recruit from institutions with different eligible populations, participation rates, and operational constraints. Learn when site-specific recruitment planning is necessary, how to allocate targets appropriately, and why achieving the overall sample size may not guarantee adequate representation across sites.
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Designing a Study You Can Defend
Staggering recruitment across study sites can make a complex study easier to manage, but different recruitment periods may introduce timing-related differences between participants. Learn when sequential site activation is appropriate, how it differs from staggered intervention delivery, and how to preserve methodological comparability.
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Designing a Study You Can Defend
A statistically justified sample size may still be impossible to recruit within the available population and study timeline. Learn how to test recruitment feasibility before data collection, calculate expected enrollment capacity, evaluate uncertainty, and identify when the research design needs revision.
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Designing a Study You Can Defend
A sample-size calculation cannot establish whether enough eligible participants can actually be recruited. Learn when a formal recruitment feasibility assessment is warranted, how to evaluate recruitment processes and progression criteria, and how to distinguish feasibility evidence from definitive study findings.
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Designing a Study You Can Defend
A calculated sample size can exceed the number of people eligible for a study. Learn when finite population correction may be appropriate, why a census does not automatically solve statistical power problems, and how to revise a study responsibly when recruitment cannot meet the original requirement.
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Designing a Study You Can Defend
Recruiting more participants than the calculated sample may protect a study against incomplete responses, unusable measurements, and anticipated exclusions. Learn how to estimate an appropriate allowance, distinguish missing data from genuinely unusable records, and avoid unnecessary recruitment that adds participant burden without improving the analysis.
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Designing a Study You Can Defend
Recruiting more participants may increase the information available for analysis, but it also creates obligations and potential burdens. Learn when additional enrollment becomes ethically difficult to justify, how to balance scientific value with participant welfare, and why an approved maximum is not necessarily a target that must be reached.
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Designing a Study You Can Defend
Research volunteers may be unusually interested, available, confident, or personally invested in the topic being studied. Learn how to recognize motivation-related selection bias, recruit beyond enthusiastic volunteers, and assess what your sample can reasonably represent.
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Designing a Study You Can Defend
Gatekeepers can help researchers access participants, but their decisions may determine who receives an invitation and whose experiences remain unheard. Learn how to define gatekeeper responsibilities, prevent discretionary selection, protect consent, and document recruitment decisions.
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Designing a Study You Can Defend
Researchers do not necessarily need to reveal their exact hypothesis in recruitment advertisements, but they must provide accurate information about the study. Learn when hypothesis disclosure may influence participation or behavior, what informed consent requires, and when limited disclosure needs ethical review.
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Designing a Study You Can Defend
Study advertisements can shape what potential participants expect before they enroll, potentially influencing who volunteers and how they respond. Learn to distinguish recruitment selection from expectancy effects and design accurate, ethically appropriate advertisements that minimize avoidable bias.
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Designing a Study You Can Defend
Participant dropout can reflect study burden, changing circumstances, accessibility barriers, dissatisfaction, or difficulties maintaining follow-up. Learn how to identify plausible dropout risks, distinguish participant characteristics from study-design problems, and develop appropriate prevention and monitoring strategies.
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