Which statistical method is used to analyze the degree of relationship between two variables?

Correlation analysis is the appropriate statistical method for examining the degree of relationship between two variables. This method assesses how changes in one variable correspond to changes in another variable, providing a correlation coefficient that quantifies the strength and direction of this relationship. A positive correlation implies that as one variable increases, the other tends to increase as well, while a negative correlation indicates that as one variable increases, the other tends to decrease. The values of the correlation coefficient range from -1 to 1, with values closer to these extremes indicating a stronger relationship.

In contrast, factor analysis focuses on identifying underlying factors that explain observed correlations among multiple variables, rather than assessing the relationship between just two. Regression analysis extends the concept of correlation by modeling the relationship and predicting values of one variable based on another, which involves a more complex approach rather than simply measuring the relationship. Bayesian approaches involve statistical methods that incorporate prior knowledge or beliefs into evidence, which is different from the straightforward relationship analysis performed in correlation. Therefore, correlation analysis specifically addresses the question of the relationship between two variables in a direct and interpretable manner.