Correlation and Causation

   Correlation and causation are two important concepts. It
is important to understand the difference between
correlation vs. causation.   With respect to research
findings, if there is a significant correlation between two
things it is merely stating that we can predict one thing
from the other.   We can predict variables without knowing
if they are casually related.  Thus, the use of the word
"correlation" does not mean causation.

Correlation Interpretation

 If A and B are correlated, it could indicate that A caused
B, B caused A, or a third variable caused both A and B.  
Two things can be correlated without there being a casual

Correlation Examples

There are many possible correlation examples.  One
example of a correlation is the association between a
supervisor's perceived humor and job satisfaction.  Decker
(1987) found that a supervisor's perceived sense of  humor
was positively correlated with people's job satisfaction.

What is Causation?

The statement that A caused B can be viewed as
suggesting that when A is manipulated it results in an
increased likelihood of a change in B.  Thus, for example,
we may observe a difference in the averages between an
experimental group and a control group when A is
manipulated in an experiment.

Single versus Multiple Causation

 There may be different types of causes.  I think we can
make a distinction between single causation and multiple
causation.  Single causation implies that a variable is
sufficient: it can influence another variable by itself.   In
contrast, multiple causation can be conceptualized as a
combination of variables that are sufficient as a group, but
none of the variables are sufficient individually.


Decker, W. H.  (1987).  Managerial humor and subordinate
Social Behavior and Personality, 15, 225-