## Exercise 4: Making a Correlation

exercise 4

### 4. Values

The box gives a matrix of **Pearson correlations** for the two
variables. Each variable has an *r* of 1 with itself, and
the two variables have an *r* of .726 with each other. SPSS
also gives the *p*-value associated with *r*.
Significant *p*-values are highlighted with ****** next
to the *r*.

Is there a convincing correlation between the variables?

SPSS can give correlation matrices for more than two variables at a time. Try some others.

Pearson’s correlation coefficient is parametric, i.e. it assumes
your data are normally distributed and are on an interval scale
with meaningful units. You might need to use Spearman’s *ρ*
(rho), which is non-parametric, i.e. appropriate for non-normally
distributed data or ordinal scale data (ranks not units). In
fact, *ρ* is the *r* of the ranks of the data: If the
data are put into order according to the variable, and assigned a
number based on this order, the Pearson correlation coefficient
for this rank number will form the Spearman coefficient.
Alternatively, you might need to use Kendall’s τ (tau), which is
like Spearman’s *ρ* but better for small data sets with many
tied ranks. Spearman and Kendall can be accessed through the same
menu as the Pearson correlation in SPSS.