Cross-sectional

Cross-sectional designs establish the prevalence of an outcome in a given population

Cross-sectional research designs can generate a measure of prevalence of a given outcome.

​All measurements of the predictors, confounders, and outcomes occur at one point in time from the overall population of interest in a cross-sectional study.

Cross-sectional designs are useful for testing associations between predictor and outcome variables, collecting large amounts of data, and generating effect sizes.

However, causal effects cannot be inferred using cross-sectional designs due to the lack of randomization.

 Many cross-sectional studies are undertaken using survey research methods.  
The blue circle represents a given population. With a cross-sectional design, researchers are taking one cross-section (red circle) out of the population (blue circle) and making inferences back to the population.
Cross-sectional research designs can establish a measure of prevalence in a given population.
Because cross-sectional designs can provide a measure of prevalence, the evidence they generate is one echelon above case-control designs.
Cross-sectional designs can generate a measure of prevalence, so they are considered a higher level of evidence in comparison to case-controls.
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