
There are several types of correlation coefficients from which to choose. The choice is based on the nature of the variables being correlated.


PointBiserial Correlation When we have one variable that is measured on a continuous scale and one variable that is measured on a dichotomous scale, then the appropriate correlation technique is the PointBiserial Correlation. The calculation is straightforward with each of the categories in the dichotomous variable being given a value of either 0 or 1. The computational formula for the PointBiserial Correlation Coefficient is:


Phi Once again the categories for each of the variables
are given a value of either 0 or 1 and again the Pearson's Correlation
is then applied to the data. It should be noted that because the
values 0 and 1 are arbitrary assigned to the categories the sign of the
phi coefficient is of minimal use. It only indicates which
diagonal had the greater concentration of scores. To estimate phi
use the formula: Phi coefficients in SPSS? Since the phi coefficient is simply the special case of the Pearson Product Moment Correlation applied to two dichotomous variables, the coefficients produced by CORRELATIONS SPSS command are phi coefficients when two dichotomous variables are involved. 

Tetachoric Correlation It is seldom used as it is a poor estimator of the population unless N is very large. Spearman's Rho In some cases we may ask subjects to rank items
along two dimensions and we may wish to correlate these rankings.
Or it may be that the researcher decides to rank the data because of extreme
data values. To calculate a Spearman's rank order Correlation is
quite simple in that once all data have been ranked, then
the Pearson Correlation formula is simply applied to the ranked data.
Spearman's rho is a measure of the linear
relationship between two variables. Kendall's Tau Coefficient Kendall based his statistic on the number of inversions in the rankings. Although there is evidence that Kendall's Tau holds up better than Pearson's r to extreme nonnormality in the data, that seems to be true only at quite extreme levels. Kendall's Coefficient of Concordance (W) Kendall's Coefficient of Concordance evaluates the relationship between a larger number of judges who rank the scores.

