Which correlation is most appropriate for ordinal data or nonparametric relationships?

Which correlation is most appropriate for ordinal data or nonparametric relationships?

• A. Spearman correlation
• B. Pearson correlation
• C. Kendall tau correlation
• D. Phi coefficient

Answer: (A)

When data are ordinal or don’t meet parametric assumptions, use a method that relies on ranks rather than raw values to measure association. Spearman correlation ranks the data and then assesses how well the relationship between those ranks appears monotonic (consistently increasing or decreasing), without assuming linearity or normal distribution. This makes it well suited for ordinal data, where only order matters, and for nonparametric relationships where the exact magnitudes aren’t reliable. Pearson correlation, in contrast, assumes interval/ratio data and a linear relationship with normality, which isn’t appropriate here. Kendall tau is another nonparametric rank-based option and is similar in spirit, but Spearman is the more common default choice for ordinal data. The Phi coefficient is meant for binary data in a 2x2 table, so it isn’t suitable for this scenario.

When data are ordinal or don’t meet parametric assumptions, use a method that relies on ranks rather than raw values to measure association. Spearman correlation ranks the data and then assesses how well the relationship between those ranks appears monotonic (consistently increasing or decreasing), without assuming linearity or normal distribution. This makes it well suited for ordinal data, where only order matters, and for nonparametric relationships where the exact magnitudes aren’t reliable. Pearson correlation, in contrast, assumes interval/ratio data and a linear relationship with normality, which isn’t appropriate here. Kendall tau is another nonparametric rank-based option and is similar in spirit, but Spearman is the more common default choice for ordinal data. The Phi coefficient is meant for binary data in a 2x2 table, so it isn’t suitable for this scenario.