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Statistical
techniques are used to explore connections between independent and dependent variables. This connection between or among variables is often
referred to as association. Association is also known as covariation
and
can be defined as measurable changes in one variable that occur concurrently with changes
in another variable. A positive association is
represented by change in the same direction (income rises with education level). Negative association is represented by concurrent
change in opposite directions (hours spent exercising and % body fat). Spurious associations
are associations between two variables that can be better explained by a third variable. As an example, if after taking cold medication for
seven days the symptoms disappear, one might assume the medication cured the illness. Most of us, however, would probably agree that the
change experienced in cold symptoms are probably better explained by the passage of time
rather than pharmacological effect (i.e., the cold would resolve itself in seven days
irregardless of whether the medication was taken or not).
Causation
There
is a difference between determining association
and causation. Causation,
often referred to as a relationship, cannot be proven with statistics. Statistical techniques provide evidence that a
relationship exists through the use of significance testing and strength of association
metrics. However, this evidence must be
bolstered by an intellectual exercise that includes the theoretical basis of the research
and logical assertion. The following presents
the elements necessary for claiming causation:
Required
elements for causation
Association
Do
the variables covary?
Precedence
Does
the independent variable vary before the affect exhibited in the dependent variable?
Plausibility
Is
the expected outcome consistent with theory/prior knowledge?
Nonspuriousness
Are
no other variables more important?
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