Two Kinds of Research
Most
research in psychology falls into one of two categories. We will learn about correlational research
today and about experimental research next time.
Correlational
Research
Correlational research
tests for statistical relationships between variables. (I have even seen it referred to as
"relational" research.) The
researcher begins with the idea that there might be a relationship between two
variables. She or he then measures the
value of each variable for a large number of cases, and checks to see if they
are in fact related. This might involve
constructing a contingency table, line graph, or scatterplot and using some
other statistical techniques that we will discuss later in the semester.
For
example, imagine that a health psychologist is interested in testing the claim
that people with more friends tend to be healthier. She surveys 500 people in her community,
asking them how many friends they have and getting some measure of their
overall health. Then she makes a
scatterplot and sees that there is a positive correlation between these
variables. Although
relationships between two quantitative variables are more likely to be called
"correlations," correlational research might involve any of the three
types of statistical relationships. For
example, imagine that the health psychologist described surveys two groups of
people: hospital patients being treated for chronic diseases and healthy
community members. Then she compares the
two groups in terms of the average number of friends they have and finds that
the healthy people have more friends.
This is also correlational research.
The same would be true if she compared the two groups in terms of the
percentage that had a best friend.
Whether or not research is correlational does not depend on whether the
variables are quantitative, categorical, or one of each.
A Problem for Correlational Research: Causal Interpretations
Sometimes
statistical relationships are interpreted in terms of changes in one variable
causing changes in the other. Let us call these causal interpretations. For example, a psychologist might claim that
having more friends is not just positively correlated with better health, but
that it actually causes people to be healthier.
One problem with correlational research, however, is that it provides
only weak evidence for causal interpretations.
This is because it is possible for Variables X and Y to be related even
though X does not cause Y. There are
essentially two reasons for this.
The
directionality problem refers to the fact that X and Y will be
correlated regardless of whether X causes Y, or Y causes X. Imagine that there is a positive correlation
between number of friends and health.
This could be because having more friends somehow causes one to be
healthier, or it could be because being healthier causes one to have more
friends. Maybe healthier people get out
of the house more and engage in more social activities, which results in their
having more friends.
The
third-variable problem refers to the fact that X and Y will also be
correlated when there is a third variable that affects both of them. Consider the friends and health example
again. Being a happier person might
cause one to have more friends and cause one to be healthier. Note that the third-variable problem is not
just that there is a third variable that is related to one or both variables;
it is that there is a third variable that causes changes in both of the other
variables, producing an incidental correlation between them. The thing to remember here is that you have to
be very careful about interpreting the results of correlational research as
evidence for causal intepretations.
Psychologists and psychology majors often remember it this way: "Correlation
does not imply causation."
But
Do Not Be Too Negative About Correlational Research
Sometimes
in a research methods course, we can spend so much time trying to show that
correlational studies do not support causal interpretations that we leave the
impression that correlational studies are not useful. This is not at all true. They are very useful for at least three
reasons. First,
correlational research is fine for establishing statistical relationships. A correlational study that shows a positive
relationship between number of friends and health has established that this is
true, even if it does not establish that more friends causes better
health. Sometimes, just knowing that two
variables are related is interesting … regardless of whether either one has a
causal influence on the other. In many
situations, after establishing that there is a relationship, researchers will
conduct more studies to determine why. Second, even
though correlation does not imply causation, causation does imply correlation. For example, if having more friends does
cause people to be healthier, then we should observe a positive correlation
between these variables. This has two
related implications. If we do observe
this positive correlation, then at least this is consistent with the causal
interpretation and perhaps should count as weak evidence for it. Also, if we do not observe this positive
correlation, then this should definitely count as evidence against the causal
interpretation. Third,
there are situations in which the major alternative to correlational research
(experimental research) cannot be conducted for practical or ethical
reasons. In such cases, correlational
research might be the best approach available.
We will discuss examples in class.