Scientific Methods II:
Correlational Research
EXAMPLES
"MARRIAGE SLOWS CANCER DEATHS“
Evidence that married people have a better
chance of surviving cancer than do singles
means that the unmarried might be good
targets for cancercancer-prevention programs.
Married people with cancer had a 23%
higher overall survival rate than the
unmarried.
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Example 2
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Isolation increases with Internet use
Psychologist Robert Kraut and his
colleagues report that greater use of the
Internet leads to shrinking social support
and happiness, and increases in
depression and loneliness…And the
findings were unexpected, Kraut says,
given that most people use the Internet for
chat lines and ee-mail, not just to isolate
themselves in mounds of electronic
information.
Example 3
“Revenge of the Beaker Bunch”..Researchers Find that
Scientists Live Longer
If it were a movie, it would probably be called "Nerds:The
"Nerds:The
Ultimate Revenge.“ A study spanning nearly 70 years
suggests that, all else being equal, scientists live longer
than nonnon-scientists… According to Friedman's study,
nonscientists are 26 percent more likely to die at any given
age than scientists. In a sample of 600 men born around
1912, Friedman's group found that only 67% of non
scientists were still alive by age 70, compared to 72% of
the scientists...”
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Correlational Research
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Correlational research examines whether 2
factors are naturally associated. [Co[Co-relation: the
degree to which one variable is related to
another.]
Examples focus on naturally occurring
relationships
„ marriage & cancer recovery
„ Internet use & depression
„ occupation & longevity
Correlations: The Basics
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Numerical range: -1.00 to +1.00
The Correlational Method
Correlation Coefficient
Positive correlations indicate that an
increase in one variable is associated with
an increase in the other.
Negative correlations indicate that an
increase in one variable is associated
with a decrease in the other.
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The Correlational Method
The Correlational Method
„Surveys
The correlational method often relies on
surveys, as well as on observational data.
Surveys are used when the variable of
interest is not easily observable.
How do we evaluate correlational
findings?
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Amount use Internet is associated with
greater depression. Why?
3 possible explanations
Using Internet Æ depression
Depression Æ greater Internet use (cannot
determine direction of causality)
A 3rd variable might be related to both
using the Internet and becoming
depressed
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3rd Variables
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People….
People….
Evaluating Correlational Research
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Correlational research cannot determine
causality.
Problems knowing direction of causality
Problems knowing whether some 3rd
variable (confounding variable) might
explain the observed correlation
How do we evaluate correlational
findings?
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Consider in context of research on TV
exposure and aggression
Start w/theoretical idea and to test specific
hypotheses.
Hypothesis: Children who watch more
violent television shows behave more
aggressively.
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TV & Violent Content
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8 out of 10 Sat. morning programs contain
violence
Sat. morning children’s shows: 20 violent
acts per hour
Average American watches 4 hrs of TV
per day, or over 1000 hrs. per year.
What is relationship between watching TV
and children’s aggressive behavior?
TV and Aggression
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Many studies
Sample correlational paradigm:
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Parents record how much TV their kids watch
Teachers record aggressive behaviors
(hitting, pushing, shoving, insulting, etc.)
IV AND DV in Correlational Research
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QuasiQuasi-Independent Variable: how much
TV kids watch (*not*
(*not* manipulated, but acts
like IV)
Dependent Variable: amount of
aggressive behavior at school
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TV and Aggression: Correlational Studies
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Conclusion: Children who watch TV that
includes violent content are more likely to
behave aggressively.
Why?
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Theory: Watching TV causes aggression
But: Maybe more aggressive kids are
attracted to watching violence on TV
(direction of causality)
Or: A 3rd variable might account for the
relationship
Third Variables
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What are some possible third variables
that could explain the correlation between
watching TV and aggressive behavior?
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Marriage and Surviving Cancer
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Direction of causality is not as much of a
problem (unlikely that surviving cancer
caused marriage!)
What are some 3rd variables that could
account for the greater survival rate of
married individuals?
Disadvantage
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Correlation cannot determine causality
Even if we measure possible third
variables (e.g., measure eating habits,
measure health care, etc.) and use
statistical techniques to “control” for them,
we still cannot be sure about the causecauseandand-effect relationship.
Then…Why do correlational research?
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Allows for the study of problems that
would be difficult to study in the lab
Can’t manipulate who is married or not,
who has a stressful job or not, gender,
race/ethnicity
Useful for generating hypotheses that can
be tested later in a controlled experiment
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Video on correlational method
Retrospective Study
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Observe some outcome (lung cancer) and
then gather information from people with
and without the outcome (e.g., about
smoking, eating habits, exercise, parenting
style)
Prospective Study
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A sample of individuals (preferably a
representative sample) is followed over
time to see whether certain experiences
predict a later outcome (e.g., whether
smoking predicts later lung cancer)
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Common Cause
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Kids who eat more sugar might be more
aggressive (during sugar high) and watch
more TV when their blood sugar drops and
they become lethargic.
Common cause would explain why we see
an association between TV and
aggression
Confounding Factor
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Something that occurs (accidentally) that
provides an alternative explanation for the
correlation. (Video: speed limit lowered,
but confound = gas crisis and less driving)
Coincidence
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Two variables are coincidentally
associated, but doesn’t happen again.
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Simpson’s Paradox
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Lurking variables can change the direction
of an association.
In video, more men than women applied to
law school, and the law school had easier
admission standards than the business
school. Thus, the overall numbers (across
both programs) appeared to show that
more men were accepted to the
professional programs.
Professional Schools
Applied Accepted
Men
360 198 55%
Women
200 88
44%
*Looks like few women admitted
Business School
Men
120 18
15%
Women
120 24
20%
*Greater % women admitted than men
Law School
Men
240 180 75%
Women
80
64
80
*Greater % women admitted than men
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