What is the parametric test for correlation between two continuous variables?

What is the parametric test for correlation between two continuous variables?

• A. Pearson's r
• B. Spearman's rho
• C. Chi-square
• D. Eta squared

Answer: (A)

Pearson correlation is the parametric method for assessing the relationship between two continuous variables because it uses the actual measured values to quantify a linear association. It assumes the data are on an interval or ratio scale, the relationship is roughly linear, and the variables are approximately normally distributed in combination (bivariate normal). The statistic, r, describes both direction and strength of that linear link, ranging from -1 (perfect negative linear) to +1 (perfect positive linear), with 0 indicating no linear relationship. To determine if the observed association is statistically significant, r is transformed into a t statistic with n minus 2 degrees of freedom. If the assumptions aren’t met (for example, if the data are ordinal or the relationship is monotonic but not linear), a nonparametric alternative like Spearman’s rho is more appropriate. Other options, such as chi-square, apply to categorical data, and eta squared is an effect size measure from ANOVA, not a correlation test.

Pearson correlation is the parametric method for assessing the relationship between two continuous variables because it uses the actual measured values to quantify a linear association. It assumes the data are on an interval or ratio scale, the relationship is roughly linear, and the variables are approximately normally distributed in combination (bivariate normal). The statistic, r, describes both direction and strength of that linear link, ranging from -1 (perfect negative linear) to +1 (perfect positive linear), with 0 indicating no linear relationship. To determine if the observed association is statistically significant, r is transformed into a t statistic with n minus 2 degrees of freedom. If the assumptions aren’t met (for example, if the data are ordinal or the relationship is monotonic but not linear), a nonparametric alternative like Spearman’s rho is more appropriate. Other options, such as chi-square, apply to categorical data, and eta squared is an effect size measure from ANOVA, not a correlation test.