What metric is used to measure correlation?

What metric is used to measure correlation?

• A. Pearson Correlation Coefficient (r)
• B. Spearman Correlation Coefficient
• C. The Phi Coefficient
• D. Kendall's Tau

Answer: (A)

Correlation between two variables is quantified by how strongly and in what direction they relate, specifically in a linear fashion. The Pearson correlation coefficient, r, is designed for this purpose: it measures the linear association by standardizing the covariance between the variables with their standard deviations. Interpreting r is straightforward—values near 1 indicate a strong positive linear relationship, values near -1 indicate a strong negative linear relationship, and values near 0 suggest little or no linear relationship. It assumes both variables are measured on interval or ratio scales and that the relationship is roughly linear without major outliers. Other coefficients serve different data types or patterns. The Spearman correlation is based on ranks and detects monotonic relationships, which may be nonlinear. The Phi coefficient is used for binary variables in a 2x2 table. Kendall’s Tau is another rank-based measure of association, often preferred with small samples or many ties. For assessing linear association between continuous variables, Pearson r is the standard choice.

Correlation between two variables is quantified by how strongly and in what direction they relate, specifically in a linear fashion. The Pearson correlation coefficient, r, is designed for this purpose: it measures the linear association by standardizing the covariance between the variables with their standard deviations. Interpreting r is straightforward—values near 1 indicate a strong positive linear relationship, values near -1 indicate a strong negative linear relationship, and values near 0 suggest little or no linear relationship. It assumes both variables are measured on interval or ratio scales and that the relationship is roughly linear without major outliers.

Other coefficients serve different data types or patterns. The Spearman correlation is based on ranks and detects monotonic relationships, which may be nonlinear. The Phi coefficient is used for binary variables in a 2x2 table. Kendall’s Tau is another rank-based measure of association, often preferred with small samples or many ties. For assessing linear association between continuous variables, Pearson r is the standard choice.