Kendall's Tau-b using SPSS Statistics - A How-To Statistical Guide by Laerd Statistics
Kendall's tau-b (τb) correlation coefficient (Kendall's tau-b, for short) is a an alternative to the nonparametric Spearman rank-order correlation coefficient. For example, if we wanted to calculate the correlation between the number . correlation coefficient, Spearman's Rho, and Kendall's Tau. SPSS Output provides a matrix of correlation coefficients for the three variables. Spearman's rho is a measure of association between rank orders. tables and conditional for 3-way to way tables), Kendall's tau-b, and Kendall's tau-c.
A symmetric measure of association between two ordinal variables that ranges between -1 and 1. Values close to an absolute value of 1 indicate a strong relationship between the two variables. Values close to 0 indicate little or no relationship.
For 2-way tables, zero-order gammas are displayed. For 3-way to n-way tables, conditional gammas are displayed. A measure of association between two ordinal variables that ranges from -1 to 1. Values close to an absolute value of 1 indicate a strong relationship between the two variables, and values close to 0 indicate little or no relationship between the variables.
Somers' d is an asymmetric extension of gamma that differs only in the inclusion of the number of pairs not tied on the independent variable.
Kendall's Tau-b using SPSS Statistics
A symmetric version of this statistic is also calculated. A nonparametric measure of correlation for ordinal or ranked variables that take ties into account. The sign of the coefficient indicates the direction of the relationship, and its absolute value indicates the strength, with larger absolute values indicating stronger relationships. A nonparametric measure of association for ordinal variables that ignores ties. When one variable is categorical and the other is quantitative, select Eta.
The categorical variable must be coded numerically. A measure of association that ranges from 0 to 1, with 0 indicating no association between the row and column variables and values close to 1 indicating a high degree of association.
Eta is appropriate for a dependent variable measured on an interval scale for example, income and an independent variable with a limited number of categories for example, gender.
Two eta values are computed: Cohen's kappa measures the agreement between the evaluations of two raters when both are rating the same object.
The relationship displayed in your scatterplot should be monotonic. In our enhanced guides, we show you how to: Just remember that if you do not test these assumptions correctly, the results you get when running a Spearman's correlation might not be valid.
Spearman's Rank-Order Correlation using SPSS Statistics
This is why we dedicate a number of sections of our enhanced Spearman's correlation guide to help you get this right. You can find out about our enhanced content as a whole hereor more specifically, learn how we help with testing assumptions here.
Spearman's correlation determines the degree to which a relationship is monotonic. Put another way, it determines whether there is a monotonic component of association between two continuous or ordinal variables.
As such, monotonicity is not actually an assumption of Spearman's correlation. Pearson's correlation coefficient assumes that each pair of variables is bivariate normal. From the menus choose: Select two or more numeric variables.
The following options are also available: For quantitative, normally distributed variables, choose the Pearson correlation coefficient. If your data are not normally distributed or have ordered categories, choose Kendall's tau-b or Spearman, which measure the association between rank orders. A value of 0 indicates no linear relationship. When interpreting your results, be careful not to draw any cause-and-effect conclusions due to a significant correlation.