![]() This leads to the conclusion that V4 does not exhibit a correlation with the other variables. This invites the conclusion that the relationship between the values of the variables V1 and V3 is stronger than the relationship between V1 and V2, even though the directions are opposite.Ĭomparisons of the data for V4, whether with V1, V2, or V3, do not reveal gradually increasing or decreasing behavior. It can also be observed that the values for the correlation between V1 and V3 are closer to the imaginary straight line than the values for the correlation between V1 and V2. It can be stated that there is a negative linear correlation between V1 and V3, just as Ohki and Bellen identified a moderate negative correlation (ρ = -0.65 p < 0.01) between average regional temperature and the incidence of venous thrombosis. In contrast, variables V1 and V3 exhibit antagonistic behavior: when the values of one increase, the values of the other reduce. identified a strong positive correlation (ρ = 0.82 p < 0.01) between cores on the Venous Symptoms Clinical Severity Scale and pain in chronic venous disease. It can be stated that there is a positive linear correlation between V1 and V2. Variables V1 and V2 exhibit simultaneously increasing values, which are distributed around an underlying imaginary (ideal) straight line, which describes the trajectory of the data. 2 - 4 Different combinations of these premises indicate a need for different techniques for correlation analysis.įigure 1 illustrates the distribution of values of four hypothetical variables (V1, V2, V3, and V4), which exhibit data that follow a normal distribution (Shapiro-Wilk, p > 0.32).īar graphs, scatter plots, and correlation coefficients ( r: Pearson, ρ: Spearman, and τ: Kendall Tau-b) for four hypothetical quantitative variables V1, V2, V3, and V4 (n = 40). The first step in analyzing correlations between two quantitative variables should be to look at a scatter plot, in order to discern whether there is a gradual variability between the sets of variables, whether this variation is monotonic (predominantly increasing or decreasing), if it follows a proportional tendency (linear), and whether the underlying distribution of the data is normal. There are many different statistical tests that explore the intensity and direction of this mutual behavior of variables, known as correlation tests. ![]() ![]() In other words, whether when the value of one variable increases, the value of another tends to increase/ or, inversely, reduce, progressively. It is common for researchers conducting clinical or biomedical studies to be interested in investigating whether the values of two or more quantitative variables change in conjunction in a given individual or object of study. ![]()
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