Designing a Study You Can Defend
Qualitative description is appropriate when researchers need a detailed, context-sensitive account of experiences, perceptions, or practices without developing a theory or pursuing a specialized methodological tradition. Learn when to choose it, what it can accomplish, and how to defend the decision.
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Designing a Study You Can Defend
A generic qualitative research design may be appropriate when a study requires systematic qualitative inquiry but does not fit the distinctive purposes of an established methodological tradition. Learn how to recognize that situation, evaluate the alternatives, and justify a coherent research design.
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Designing a Study You Can Defend
A mixed-methods study can incorporate a qualitative or quantitative component within a larger research design. Learn when embedding is appropriate, how primary and secondary components relate, and how to distinguish meaningful integration from simply adding another data collection method.
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Designing a Study You Can Defend
Research comparisons can involve individuals, groups, time points, experimental conditions, places, or exposures. Learn how to choose the appropriate unit of comparison, recognize differences in analytical levels, and avoid conclusions that your design cannot support.
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Designing a Study You Can Defend
Within-participant comparisons examine differences involving the same individuals, while between-participant comparisons examine differences across individuals or groups. Learn how to choose between them, evaluate their assumptions, and avoid confusing observed change with causal evidence.
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Designing a Study You Can Defend
Concurrent comparisons examine groups or conditions during overlapping periods, while historical comparisons use observations from earlier periods. Learn how to choose between them, evaluate temporal confounding, and determine when historical evidence can support meaningful conclusions.
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Designing a Study You Can Defend
Not every research question requires a comparison group. Descriptive, exploratory, interpretive, and some evaluative studies may be appropriately designed without one. Learn when this choice is methodologically justified, how it differs from a weak causal design, and what conclusions noncomparative evidence can legitimately support.
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Designing a Study You Can Defend
A causal interpretation depends on more than a statistically significant relationship. Learn how well-defined interventions, credible counterfactual comparisons, randomization, temporal ordering, confounding control, consistent measurement, and sensitivity analyses strengthen causal claims, and why no single feature guarantees causation.
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Designing a Study You Can Defend
When different research designs produce compatible findings, confidence in a causal explanation may increase. However, repeated results do not automatically eliminate shared biases. Learn how cross-design replication, triangulation, independent assumptions, and evidence convergence can strengthen causal interpretation without overstating certainty.
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Designing a Study You Can Defend
A survey may refer to a broader research strategy or to a method of collecting information, depending on how the term is used. Understand these methodological distinctions and learn how to describe your study accurately without confusing the research design with the questionnaire.
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Designing a Study You Can Defend
Primary and secondary data are not simply two ways of obtaining the same evidence. Whether you collect new data or work with existing data changes which parts of the study you can design yourself and which constraints you inherit.
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Designing a Study You Can Defend
Record linkage errors can distort research findings even when most records are successfully matched. Learn how false and missed matches affect study design, measurement, selection, and statistical analysis.
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Designing a Study You Can Defend
Clustering occurs when observations share participants, institutions, environments, or treatment assignments. Learn when it must be considered, how it affects research design and precision, and which analytical approaches can accommodate it.
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Designing a Study You Can Defend
Non-probability sampling has important inferential limitations, but thoughtful research design can reduce avoidable weaknesses. Learn how to improve recruitment coverage, document selection, use auxiliary information, strengthen causal comparisons, and report conclusions that remain defensible.
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Designing a Study You Can Defend
A defensible research design is not simply one that follows a recognized method. It is one in which the research question, evidence, procedures, analysis, and conclusions are coherently aligned and the important methodological choices can be justified.
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Designing a Study You Can Defend
Not every threat to internal validity is equally consequential. Learn how to identify the weaknesses most likely to distort your study's causal conclusion, evaluate the evidence for each threat, and prioritize methodological safeguards without relying on a generic checklist.
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Designing a Study You Can Defend
Statistical adjustment cannot reliably repair every weakness in a research design. Learn how to prevent major threats to internal validity through credible assignment procedures, appropriate comparison groups, consistent measurement, and carefully planned implementation.
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Designing a Study You Can Defend
Explaining confounding control requires more than listing adjusted variables or acknowledging possible bias. Learn how to distinguish addressed and unresolved confounding, evaluate its consequences, and communicate the strength of your causal conclusions.
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Designing a Study You Can Defend
Missing data can undermine a study long before statistical analysis begins. Learn how to anticipate missingness, protect essential measurements, reduce preventable data loss, and design analyses that acknowledge what missing-data methods can and cannot recover.
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Designing a Study You Can Defend
A research design may become unacceptable when predictable missingness prevents credible estimation of its primary outcome or effect. Learn how to distinguish manageable missing data from fundamental design failures, evaluate alternatives, and decide when the research question or measurement strategy must change.
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Designing a Study You Can Defend
Rare outcomes present a research design problem because relatively few participants experience the event of interest. Learn when case-control, cohort, registry-based, and other designs are appropriate, how to evaluate efficiency and causal validity, and why the rarest event does not always require the most complicated methodology.
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Designing a Study You Can Defend
Rare exposures require a different sampling strategy from rare outcomes. Learn why cohort designs are often efficient when exposed individuals can be identified, how to construct valid comparison groups, and when registries, case-control studies, or alternative approaches may provide stronger evidence.
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Designing a Study You Can Defend
Bayesian research designs may help researchers make better use of limited observations by incorporating defensible prior information and expressing uncertainty through posterior distributions. Learn when these approaches are useful, how prior assumptions influence conclusions, and why Bayesian methods do not automatically overcome small-sample limitations.
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Designing a Study You Can Defend
Exact tests and specialized small-sample methods can improve statistical inference when conventional approximations are unreliable. Learn why these methods should be considered during research design, what assumptions they require, and why they cannot compensate for inadequate sampling or poor measurement.
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Designing a Study You Can Defend
When researchers cannot reliably identify or contact their target population, recruitment difficulties may threaten more than sample size. Learn how to distinguish hidden populations from inaccessible sampling frames, select defensible sampling strategies, protect participants, and revise research objectives when representative recruitment is infeasible.
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