The effect size index r (Pearson’s r measure of the strength of the relationship between two variables) was
used since it is more appropriate for measures of association and one of the most widely
used effect sizes. The effect size index r can vary from −1 (a perfect negative relationship)
to 1 (a perfect positive relationship) with 0 as no relationship between two variables.
According to Cohen’s (1988) rough characterization, r=0.1 is deemed as a small effect size,
r=0.3 a medium effect size, and r=0.5 as a large effect size, in light of the nature and
characteristics of behavioral or social sciences. It is noted that for the physical sciences
correlation coefficients have a different order of magnitude. In addition, how much of the
variability in the dependent variable is associated with the variation in the independent
variable are used for examining proportion of variance such as R2 in the regression analysis.
Here, the effect size index f2 was used since it is more appropriate for multiple regression
methods. Again, in accordance with Cohen’s (1988) rough categorization, f2=0.02 is
deemed as a small effect size, f2=0.15 a medium effect size, and f2=0.35 as a large effect
size.