Kendall’s Rank Correlation Coefficient

  • Assumptions to check 

    • Data hast to be at least an ordinal scale

    • Variables represent paired observations

    • There is a monotonic relationship between the two variables with no significant outliers

  • Report design in Methods section

    • Report on variables

    • Name the statistical package or program used in the analysis

  • Report statistics in Results section:

    •  correlation coefficient tau_b, p-value

  • Example:

    • Methods section:  To evaluate the correlation between factors (height and weight) the non-parametric Kendall’s rank correlation analysis was used. The analysis was performed in Jamovi software (RRID:SCR_016142).

    • Results section: The non-parametric Kendall’s rank correlation analysis was used, and it showed a high and significant positive correlation between height and weight (tau_b(98) = .63, p < .001). The scatterplot, with a monotonic trend line, showed no violation of the monotonic relationship assumption and no significant outliers.