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A stepped-wedge design introduces an intervention to different clusters at different times, often using randomized rollout sequences. Understand how the design works, when it offers advantages over a parallel trial, and why time-related confounding requires particular attention.
Read Guide →Stepped-wedge and parallel cluster-randomized trials can evaluate group-level interventions, but they differ in allocation, timing, analysis, and implementation demands. Learn when phased randomized rollout provides a genuine advantage and when a simpler parallel trial may be preferable.
Read Guide →A naturally staged intervention rollout can create an opportunity to evaluate a policy or program without controlling its implementation. Learn when staggered adoption supports credible research, how to identify comparison groups, and why implementation timing and pre-intervention trends matter.
Read Guide →Interventions introduced at different times can appear effective because outcomes were already changing or other events occurred during implementation. Learn when time-related confounding threatens rollout evaluations, how it differs from ordinary secular trends, and what researchers can do to strengthen causal inference.
Read Guide →Quasi-experimental designs can evaluate interventions when random assignment is unavailable or inappropriate. Learn when these designs are justified, which causal assumptions matter, and how to distinguish a credible quasi-experiment from a weak before-and-after comparison.
Read Guide →Quasi-experimental and observational studies can both involve nonrandomized exposure, making their boundaries less obvious than textbooks suggest. Learn how intervention assignment, causal identification, comparison strategies, and disciplinary conventions distinguish the approaches.
Read Guide →Difference-in-differences can estimate intervention effects by comparing outcome changes between treated and comparison groups. Learn when this strategy is appropriate, which assumptions matter, and why having before-and-after data is not enough.
Read Guide →Regression discontinuity can support credible causal inference when intervention assignment changes at a known threshold. Learn when the design is appropriate, how sharp and fuzzy designs differ, and why a cutoff alone is insufficient.
Read Guide →An interrupted time-series design examines whether an intervention changes an outcome's established pattern over time. Learn when this quasi-experimental approach is appropriate, how to interpret changes, and what can threaten causal inference.
Read Guide →Choosing a quasi-experimental design requires more than matching a method to available data. Learn how treatment assignment, counterfactual assumptions, and identification opportunities determine which design can support a credible causal conclusion.
Read Guide →A quasi-experimental label does not guarantee stronger causal evidence than an observational study. Learn when weak comparisons undermine quasi-experiments, how rigorous observational designs can be more informative, and what researchers should prioritize when evaluating evidence.
Read Guide →A natural experiment uses exposure variation created by circumstances outside the researcher's control to investigate causal effects. Learn what qualifies as a natural experiment, why natural assignment does not automatically mean random assignment, and how researchers evaluate its credibility.
Read Guide →Natural experiments and quasi-experiments both investigate causal effects without conventional researcher-controlled randomization, but they are not identical concepts. Learn how they differ in exposure assignment, research design, causal assumptions, and interpretation.
Read Guide →A naturally occurring exposure or policy change can create an opportunity for causal research, but only when its assignment mechanism supports a credible counterfactual. Learn how to assess exposure variation, comparison groups, timing, confounding, and the assumptions required for valid inference.
Read Guide →A natural experiment can be investigated after the relevant event has occurred, but researchers must reconstruct its assignment mechanism and establish a credible counterfactual. Learn how to design a retrospective evaluation, assess historical data, and avoid analytical decisions that undermine causal inference.
Read Guide →A natural experiment may coincide with other policies, disruptions, or institutional changes that affect the same outcome. Learn how concurrent events threaten causal identification, when comparison groups help, and how to distinguish intervention effects from competing explanations.
Read Guide →Natural experiments can provide stronger causal evidence than ordinary observational comparisons when external exposure variation creates a credible counterfactual. Learn which identification features matter, what assumptions remain necessary, and why the natural-experiment label alone does not establish stronger evidence.
Read Guide →Calling a study a natural experiment can imply stronger causal identification than the evidence supports. Learn when the label becomes misleading, which assumptions researchers must defend, and how to describe externally generated exposure without overstating causal conclusions.
Read Guide →Case study research investigates a clearly bounded case in depth and in relation to its real-world context. Studying one person, classroom, institution, event, or site does not automatically make a project a case study because the design depends on how the case is defined and investigated.
Read Guide →The unit of analysis determines what a case study is fundamentally investigating. Learn how to distinguish the case from its participants, data sources, and embedded units, and how to align the unit of analysis with research questions, sampling, analysis, and conclusions.
Read Guide →A case study needs clear boundaries, but these are not necessarily limited to one institution, location, or period. Learn how to define what belongs inside a case, distinguish case boundaries from sampling decisions, and handle overlapping organizational, spatial, event-based, and temporal boundaries.
Read Guide →Should you investigate one case intensively or compare several cases? Learn when single-case and multiple-case study designs are appropriate, how replication logic differs from statistical sampling, and why the number of cases should follow the research question rather than assumptions about methodological superiority.
Read Guide →Intrinsic, instrumental, and explanatory case studies are not three mutually exclusive research designs. Learn how Stake's distinction between intrinsic and instrumental interests differs from Yin's explanatory orientation, how the approaches may overlap, and what each means for research questions, case selection, and analysis.
Read Guide →Case selection should be driven by what a case can contribute to the research question, not merely by access. Learn how to justify typical, unusual, critical, and theoretically informative cases, distinguish case selection from participant sampling, and avoid selection decisions that undermine the study's conclusions.
Read Guide →A typical case can help researchers understand how a phenomenon operates under relatively ordinary conditions. Learn how to define typicality, select cases using relevant evidence, distinguish typicality from statistical representativeness, and avoid assuming that findings from one ordinary case apply to an entire population.
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