r squared, or the coefficient of determination, reflects the magnitude of an association between variables and is most appropriate when comparing the magnitude of different correlations. A higher value of Pearson’s r indicates a stronger relationship between the variables. To determine the coefficient of determination, this r value is squared.
Example: if you have independent variables of age and GMFCS level and a dependent variable of TUG test time, you could have an r squared value for age vs TUG test time association (hypothetically .017) and a different r squared value for GMFCS level vs TUG test time association (hypothetically .068). The r squared value for the GMFCS level vs TUG test time association (.068) is a much greater magnitude than the r squared value for age vs TUG test time association (.017), therefore, GMFCS level could be said to explain 4 times as much of the variance than age. To interpret this clinically, we could say that a child’s GMFCS level has a greater magnitude of association with TUG test times compared to age.
(r should be in italics, however, the forum post box won’t let me change the font).