Designing a Study You Can Defend
A dataset with thousands of records may contain far fewer independent participants or experimental units. Learn how to distinguish observations from independent replication, calculate illustrative design effects, and prevent misleading sample size and statistical precision.
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Designing a Study You Can Defend
Researchers may recruit schools, hospitals, households, or organizations while analyzing outcomes from individuals within them. Learn when recruitment and analytical units can differ, how this affects selection and clustering, and what must be reported.
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Designing a Study You Can Defend
Researchers can randomize classrooms, hospitals, or organizations while measuring outcomes from individuals within them. Learn how to distinguish randomization and measurement units, plan appropriate analyses, and avoid misleading treatment-effect estimates.
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Designing a Study You Can Defend
Recruiting individuals through selected schools, hospitals, organizations, or communities can make research more feasible, but it also affects population coverage, selection probabilities, statistical dependence, and precision. Learn how to design and analyze these studies without confusing the number of participants with the number of independent groups.
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Designing a Study You Can Defend
Assigning treatment to classrooms, hospitals, or communities while measuring individual outcomes creates a clustered intervention design. Learn how this affects treatment-effect estimation, statistical independence, sample size, recruitment bias, and causal interpretation.
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Designing a Study You Can Defend
A study with hundreds of participants may still have too few independent clusters to support reliable inference. Learn how to determine the appropriate number of participants and clusters, calculate illustrative design effects, and plan sample size according to the research question and assignment structure.
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Designing a Study You Can Defend
Pseudoreplication can arise when researchers treat multiple measurements or participants within one treatment-assigned group as independent experimental replications. Learn how to identify genuine treatment replication, distinguish subsamples from experimental units, and determine whether a mismatched analysis can be corrected.
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Designing a Study You Can Defend
Interventions may operate at the classroom, school, hospital, or community level while outcomes are measured on individuals. Learn how to align intervention delivery, treatment assignment, outcome measurement, sampling, and statistical inference within a defensible research design.
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Designing a Study You Can Defend
A defensible sample begins with a clearly defined population. Learn how to move from your research question to the people or units you actually recruit without claiming more than your design can support.
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Designing a Study You Can Defend
Your data usually come from a sample, but your research question may concern a much larger population. Understanding that distinction is essential for choosing participants and interpreting what your findings actually mean.
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Designing a Study You Can Defend
A well-defined target population establishes who or what your research is intended to describe or explain. Learn how to specify population boundaries, align them with your research question, distinguish them from recruitment restrictions, and avoid unsupported generalization.
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Designing a Study You Can Defend
A target population that is too broad may exceed what the study can meaningfully investigate, while one that is unnecessarily narrow may limit relevance and generalizability. Learn how to establish defensible population boundaries using the research question, estimand, relevant heterogeneity, and intended application of the findings.
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Designing a Study You Can Defend
Choosing study sites before defining the target population can allow convenience and institutional access to determine the scope of research conclusions. Learn when population definition should precede site selection, how to select appropriate research settings, and when site-first research designs are justified.
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Designing a Study You Can Defend
A research population exists within a temporal context, but the dates of data collection do not necessarily define that population. Learn when time should determine eligibility, how to distinguish population reference periods from observation periods, and why temporal boundaries matter for valid research conclusions.
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Designing a Study You Can Defend
Restricting a research population can improve conceptual clarity, measurement comparability, and intervention relevance, but it may also limit who the findings apply to. Learn when narrowing the population is justified, why homogeneity does not automatically guarantee internal validity, and how to balance focused research questions with broader applicability.
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Designing a Study You Can Defend
Inclusion criteria should identify the participants or other units necessary to answer your research question, not merely describe who is easiest to recruit. Learn how to translate a target population into clear, operational eligibility rules, avoid unnecessary restrictions, and ensure that participant selection supports the intended inference.
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Designing a Study You Can Defend
Inclusion criteria should be determined by more than the characteristics of participants who are easiest to recruit. Learn how the research question, target population, study design, intervention requirements, measurement validity, ethics, and generalizability should inform eligibility decisions.
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Designing a Study You Can Defend
Exclusion criteria should protect participants and preserve meaningful research inference without unnecessarily removing relevant population groups. Learn which scientific, ethical, and methodological factors justify exclusion, when alternative safeguards are preferable, and how to assess the consequences of restrictive eligibility.
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Designing a Study You Can Defend
Safety concerns require careful evaluation, but not every risk justifies excluding participants. Learn how to distinguish genuine contraindications from manageable risks, assess alternatives to exclusion, and protect participants without unnecessarily narrowing the research population.
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Designing a Study You Can Defend
Excluding everyone with a potential confounding characteristic may seem like a straightforward way to improve research validity, but excessive restriction can change the population, reduce generalizability, and sometimes introduce new biases. Learn when restriction is justified and when measurement, randomization, stratification, or statistical adjustment provides a better approach.
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Designing a Study You Can Defend
Exclusion criteria can protect participants and strengthen research design, but accumulating too many restrictions may produce a sample unlike the population researchers intend to understand. Learn how to recognize excessive exclusions, evaluate their cumulative consequences, and preserve meaningful population coverage without compromising scientific or ethical requirements.
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Designing a Study You Can Defend
Broad eligibility can improve population coverage, but insufficiently defined criteria may combine participants whose experiences, exposures, or outcomes are not meaningfully comparable. Learn when heterogeneity threatens interpretation, how to distinguish useful diversity from problematic variation, and how to define eligibility without unnecessarily narrowing the population.
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Designing a Study You Can Defend
Researchers can determine participant eligibility through self-report, administrative or clinical records, objective testing, and professional judgment. Each method has different strengths, limitations, and implications for bias. Learn how to select and combine eligibility assessment methods that match your research question, population, and ethical requirements.
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Designing a Study You Can Defend
Exclusion criteria may appear scientifically neutral while disproportionately preventing certain populations from participating in research. Learn how to identify unnecessary restrictions, distinguish eligibility from accessibility barriers, evaluate the consequences for validity and equity, and develop more inclusive recruitment procedures without compromising participant safety.
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Designing a Study You Can Defend
The appropriate sampling method depends on what your study is trying to learn, not simply which technique is easiest or most familiar. Learn how to select a sampling strategy that matches population estimation, causal inference, prediction, or qualitative inquiry while accounting for population coverage, selection bias, feasibility, and generalizability.
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