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Descriptive research can be conducted without a formal hypothesis when its purpose is to characterize a population or phenomenon. Learn when research questions are sufficient and when a prespecified hypothesis may be useful.
Read Guide →A defensible prevalence study requires more than counting how many participants have a particular characteristic. Learn how to define the target population, establish a valid case definition, select an appropriate sample, estimate prevalence, and interpret the findings without overstating what the design can establish.
Read Guide →Describing patterns over time requires comparable observations, an appropriate temporal design, and careful interpretation of change. Learn how to select measurement periods, distinguish population trends from individual trajectories, and avoid attributing observed changes to causes the study cannot establish.
Read Guide →Collecting more variables does not necessarily make descriptive research more comprehensive. Learn how excessive measurement can weaken conceptual focus, increase respondent burden, and produce fragmented findings, and how to retain the characteristics that genuinely matter.
Read Guide →A study does not become analytical merely because it compares groups or uses statistical tests. Learn how research objectives, estimands, analytical strategies, and intended claims determine when a descriptive investigation crosses into analytical research.
Read Guide →Descriptive research can establish important facts about populations, characteristics, and observed patterns, but not every conclusion follows from those observations. Learn which claims require additional evidence and how to write conclusions that are informative without overstating what the design can establish.
Read Guide →A cross-sectional study may be the right choice when researchers need to describe a population or examine relationships using observations from a defined period. Learn when the design fits, what evidence it requires, and when another approach would provide a more defensible answer.
Read Guide →Cross-sectional studies can describe populations and examine associations, but some conclusions require evidence they may not provide. Understand their limitations concerning causation, temporal order, incidence, individual change, selection bias, and generalization, including the important exceptions.
Read Guide →Cross-sectional associations rarely justify causal conclusions on their own, but the design label does not settle every causal inference question. Learn which assumptions matter, why temporal order is important, and when additional historical evidence or a stronger design is necessary.
Read Guide →Temporality concerns whether an exposure occurred before an outcome, a fundamental consideration in causal reasoning. Learn why cross-sectional studies often struggle to establish temporal order, when timing can still be inferred, and how to avoid overstating findings.
Read Guide →Repeated cross-sectional surveys can reveal how population characteristics, attitudes, and behaviors change over time without following the same individuals. Learn what these designs establish, how to make survey waves comparable, and why observed population trends should not be mistaken for individual change.
Read Guide →Repeated cross-sectional and longitudinal studies both examine phenomena over time, but they answer different questions. Learn when independent population samples are sufficient, when tracking the same individuals is necessary, and how sampling, attrition, and population change influence design choice.
Read Guide →A cohort study may be appropriate when researchers need to examine how outcomes develop over time among people with different exposures or characteristics. Learn what cohort designs can establish, their methodological requirements, and when another design may be more defensible.
Read Guide →Prospective and retrospective cohort studies both examine outcomes in relation to earlier exposures, but they differ in how information is obtained and when the research is initiated. Learn how to choose between them based on record quality, measurement requirements, feasibility, and threats to validity.
Read Guide →Cohort studies can contribute to causal inference when their design and analysis support a clearly defined causal question. Learn why following participants over time is not enough, which assumptions matter, and how researchers can distinguish temporally ordered associations from defensible causal effects.
Read Guide →Choosing a comparison group is central to estimating exposure-related differences in cohort studies. Learn how to identify an appropriate unexposed population, align eligibility and follow-up, evaluate comparability, and avoid selection procedures that undermine causal inference.
Read Guide →Cohort studies can provide valuable temporal evidence, but their costs and duration may exceed what a research question justifies. Learn how to assess feasibility, recognize when follow-up becomes disproportionate, and choose alternatives without compromising the evidence your study needs.
Read Guide →Controls should represent people who could have contributed cases under the study's eligibility and sampling rules. Learn why this principle matters, how eligibility changes over time, and what happens when controls could never experience the outcome being studied.
Read Guide →Matching cases and controls can improve efficiency or address particular confounding concerns, but it is not always necessary or beneficial. Learn when matching is justified, how different matching strategies work, and what their implications are for analysis and interpretation.
Read Guide →Cases and controls may remember or report previous exposures differently, potentially distorting an observed association. Learn how recall bias develops, which prevention strategies are most defensible, and how to evaluate its possible influence on study findings.
Read Guide →A case-control study can investigate several exposures associated with the same outcome, but measuring more variables does not automatically improve the research. Learn how to prioritize exposures, plan adjusted analyses, assess interactions, and distinguish confirmatory findings from exploratory associations.
Read Guide →Not every question about change requires longitudinal data, but some cannot be answered credibly without observing participants or units over time. Learn how to recognize questions involving trajectories, temporal ordering, transitions, and within-person change, and decide whether repeated measurements are methodologically necessary.
Read Guide →Two time points may be enough to estimate an observed difference, but more are usually needed to investigate trajectories, rates of change, and nonlinear patterns. Learn how to determine the number of measurement occasions from your research question, analytical model, expected change, and practical constraints.
Read Guide →Two measurements of the same participants can provide legitimate longitudinal evidence, but they cannot reveal the trajectory between observations. Learn what a two-wave design can establish, why some questions require more measurements, and how to judge whether two time points are sufficient.
Read Guide →Not every variable in a longitudinal study needs to be measured at every occasion. Learn how to distinguish stable characteristics from changing constructs, determine when repeated measurement is essential, and design a schedule that preserves analytical validity without unnecessary participant burden.
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