# Cross-sectional

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

Cross-sectional research designs can generate a measure of

All measurements of the

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

**of a given outcome.**__prevalence__All measurements of the

**,**__predictors__**, and**__confounders__**occur at one point in time from the overall population of interest in a cross-sectional study.**__outcomes__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.

Because cross-sectional designs can provide a measure of

**, the evidence they generate is one echelon above**__prevalence__**designs.**__case-control__Click on the

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