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In medical research, epidemiology, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data. [definition needed]
[1] [2] Hierarchy of study design, for example using a case-study, ecological study, cross-sectional, case-control, cohort, or experimental, although not always in this order is a general rule to a high "strength of evidence" of a clinical study. [3] [4] [5]
Rather than studying particular individuals across that whole period of time (e.g. 20–60 years) as in a longitudinal design, or multiple individuals of different ages at one time (e.g. 20, 25, 30, 35, 40, 45, 50, 55, and 60 years) as in a cross-sectional design, the researcher chooses a smaller time window (e.g. 20 years) to study multiple ...
A hierarchy of evidence, comprising levels of evidence (LOEs), that is, evidence levels (ELs), is a heuristic used to rank the relative strength of results obtained from experimental research, especially medical research. There is broad agreement on the relative strength of large-scale, epidemiological studies.
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
Cross-sectional study: involves data collection from a population, or a representative subset, at one specific point in time. Longitudinal study: correlational research study that involves repeated observations of the same variables over long periods of time. Cohort study and Panel study are particular forms of longitudinal study.
In a typical experimental study, there will be at least one "experimental" condition (e.g., "treatment") and one "control" condition ("no treatment"), but the appropriate method of grouping may depend on factors such as the duration of measurement phase and participant characteristics: Cohort study; Cross-sectional study; Cross-sequential study
Critical appraisal checklists help to appraise the quality of the study design and (for quantitative studies) the risk of bias. Critical appraisal tools for cross-sectional studies are the AXIS, [4] JBI, [5] Nested Knowledge [6] tools; for randomised controlled trials are Nested Knowledge, [6] Cochrane Risk of Bias Tool, [7] [8] JBI tool [5 ...