The extent to which two variables are related and to which one score is, consequently, predictable by the other, is termed as a correlation. The correlation coefficient therefore indicates the extent that the regression line fits the plot. This chapter discusses the correlation between two nominal variables. Cramer’s V is used when student wishes to know the strength of a relationship between two nominal variables. The most commonly used rank correlation is the Spearman’s rank correlation coefficient. The coefficient is a statistic expressing the extent to which a relationship exists between the ranks of two variables X and Y (for example wealth and happiness). Student can use a scatter plot to record the scores of a group of respondents on two variables, for example X and Y. The scale for the X scores is found on the horizontal axis; on the vertical axis student can find the scale for the Y scores.