Although scatterplots give you a general feel for the extent to which a relationship exists between two variables, they leave a lot of room for interpretation. For this reason one usually computes a correlation coefficient to determine the degree of linear relationship between two variables. Correlations coefficients range from -1 to 1. Correlations closer to zero indicate weak relationships whereas those closer to 1 and -1 indicate a strong positive and negative relationship, respectively.

Below is a correlation matrix summarizing the various correlations among the study variables. From the table, you can see that all correlations are positive indicating that higher scores in one variable are always associated with higher scores on the other. The strongest correlation observed was between arm strength and work simulations (r = .686). The weakest relationship was between ratings and work simulations (r = .1681).

Now examine the rest of the correlations.

 
                      - -  Correlation Coefficients  - -
 
             ARM        GRIP       RATINGS    SIMS
 
ARM          1.0000      .6298      .2213      .6860
            (  147)    (  147)    (  147)    (  147)
            P= .       P= .000    P= .007    P= .000
 
GRIP          .6298     1.0000      .1833      .6398
            (  147)    (  147)    (  147)    (  147)
            P= .000    P= .       P= .026    P= .000
 
RATINGS       .2213      .1833     1.0000      .1681
            (  147)    (  147)    (  147)    (  147)
            P= .007    P= .026    P= .       P= .042
 
SIMS          .6860      .6398      .1681     1.0000
            (  147)    (  147)    (  147)    (  147)
            P= .000    P= .000    P= .042    P= .
 
 
(Coefficient / (Cases) / 2-tailed Significance)