Which data analysis method is commonly used in observational studies?

Regression analysis is a commonly used data analysis method in observational studies due to its ability to assess the relationship between independent and dependent variables while controlling for potential confounding factors. Observational studies typically do not employ randomization, making it important to analyze the data in a way that can account for the inherent biases and variance present in non-randomized data.

Through regression analysis, researchers can quantify the strength and direction of the association between variables. It provides insights into how changes in independent variables affect the outcomes measured, allowing researchers to make predictions and adjust for confounding variables by including them as covariates in the model. This is critical in observational studies where the aim is often to understand associations rather than causation.

Other methods listed serve different purposes or are less applicable to the specifics of observational studies. For example, randomized controlled trials are designed to test causality through controlled environments. Qualitative analysis focuses on exploring non-numeric data, and cross-over analysis is used when the same participants receive multiple interventions sequentially, which is not typical in observational designs. Thus, regression analysis stands out as the suitable method for the statistical analysis of data in observational studies.