"5:833' SPSS: Computing the Pearson Correlation Coefficient (r is for sample, p is for population) SPSS computes r as well as the mean and standard deviation for the Xs and Y5. (You may have more than two variables and correlate all pairs of variables.) This example shows a person's extroversion level correlated with aggression level (Helman, 2014). Participant Extroversion Aggression 1 14 23 2 12 1O 3 10 11 4 13 18 5 19 20 6 20 18 7 9 10 8 4 9 Enter the data: Name the two variables and enter the scores in the two columns. Select the correlation: On the menu bar, select analyze then drop down to correlate and slide over to bivariate. The bivariate correlation box appears. Be sure Pearson is checked. Select the variable(s): Move the two variables you want to correlate to variables. Select descriptives: Click options and then check means and standard deviations. Click continue. The output: Click ok. In the descriptive statistics table are the M and s for each variable. In the correlations table is the correlation matrix. The matrix contains the correlation coefficients produced when the variable in every row is correlated with the variable in every column. Thus, in the first row the meaningless r between extroversion and extroversion scores is +1.00. However, the r that we seek between extroversion and aggression is +.747. (If it were negative, a minus sign would appear.) If we had a third variable. we would see the r between it and extroversion and between it and aggression. You will see an * by the correlation coefficient that is statistically significant at the 0.05 level (two-tailed). If you have significant results, calculate the coefficient of determination and report it as well as explain it. r2: .747x .747: .558