Correlations, measured by the Pearson's correlation coefficient (denoted by "r"), measure the strength and direction of the correlation of two variables. R is always between -1 (meaning a strong negative correlation) and 1 (meaning a strong positive correlation). If r=0, there is no correlation between the two variables. In this case, r=.75, which shows a strong positive correlation between IQ and grade point average (GPA). That is, as IQ increases, so does the GPA.
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Correlations, measured by the Pearson's correlation coefficient (denoted by "r"), measure the strength and direction of the correlation of two variables. R is always between -1 (meaning a strong negative correlation) and 1 (meaning a strong positive correlation). If r=0, there is no correlation between the two variables. In this case, r=.75, which shows a strong positive correlation between IQ and grade point average (GPA). That is, as IQ increases, so does the GPA.
There are many assumptions that have to be satisfied in order to use Pearson's correlation coefficient. One of these is that the data have no significant outliers and that they are normally distributed (along the bell curve). However, some studies have shown that IQ is not not normally distributed (see Cyril Burt's "Is Intelligence Distributed Normally" from 1963). This may be in part because of poverty and other factors that result in IQ not being normally distributed in the United States.
Another test that can be used to measure the degree of correlation between two variables is Spearman's rank correlation. This test does not assume the normal distribution of data.
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