BHV 390
Cause and Effect
Kimberly Porter Martin, Ph.D.
Requirements for Cause
and Effect
1. The causal variable and the effect
variable must be consistently, but not
necessarily always associated with one
another.
2. The causal variable must ALWAYS
preceed the effect variable in time.
3. All confounding/lurking variables that
might confuse the relationship between
cause and effect must be eliminated.
1. Consistent Association of
Cause and Effect Variables
If one thing causes another, then there
should be a strong association (in
statistical terms, a strong correlation)
between the two.
Eg. Striking a match and fire. Striking a
match always causes a fire, at least at the
tip of the match. But fires are not always
caused by matches, so the
correlation/association will not be perfect,
that is, fire will not always be associated
with striking a match.
2. The Cause Must Always
Preceed the Effect
For something to be the cause, it logically must
preceed the thing it is causing. For one thing to
flow from another, the producer that must exist
before the product. This flows from basic logic.
Eg. For a fire to be produced from the striking of a
match, the match must exist and be struck
before the fire can be produced. If the fire
comes before the match is struck, then logic
dictates that the fire cannot have been caused
by the striking of the match.
3. All Confounding or Lurking
Variables Must Be Eliminated
It is possible for two things to be consistently
associated with one another, and both be
caused by a third variable. This third
variable is called a “confounding” or “lurking”
variable.
Eg. It is a statistical fact that the number of
drowning deaths and the amount of ice
cream sold in beach communities are highly
correlated (associated) with one another.
Drowning deaths do not cause ice cream
sales, and ice cream sales do not cause
drowning deaths. Both are caused by hot
weather/the summer season.
3. All Confounding or Lurking
Variables Must Be Eliminated (con’t)
Eg. It is a statistical fact that the amount of alcohol
sold is highly correlated (associated) with the
salaries of teachers. This means that as teacher’s
salaries go up, so do alcohol sales, OR as alcohol
sales go up, so do teacher’s salaries.
Neither of these variables is causing a change in the
other. Instead, a third variable, the state of the
general economy is causing changes in both. As
the economy improves, teachers are paid more,
and people have more income to spend on alcohol.
Important!
It is rare that social research can
demonstrate clear and unambiguous causal
relationships between two variables.
Usually, social research can only
demonstrate associations/correlations
between variables, because it is hard to
show which variable came first, and to
eliminate ALL confounding/lurking variables.
The exception are demographic variables
such as age, gender and ethnicity. It would
be logically silly to say that income would
cause a difference in gender. Therefore we
can assume that a difference in income is
caused by gender.
Experiments and Causality
•
Experiments are considered the best
method for attempting to demonstrate
causality.
1. They show that a change in the dependent
variable (DV) is consistently associated with
the independent variable (IV).
2. The procedures insure that the IV always
comes before any change in the DV.
3. The experimental process is designed to
control/eliminate as many lurking variables
as possible, and only allow the IV to be a
causal factor
Conclusions
• Researchers using any method other
than experiments should be very
careful about claiming that they have
demonstrated that one thing causes
another.
• A STATISTICAL RELATIONSHIP LIKE
A CORRELATION DOES NOT MEAN
THAT ONE THING CAUSES
ANOTHER!
Powerpoint Study Guide
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Cause Variable
Effect Variable
Lurking Variable
Time Sequence
Association