Experiments Exploring Cause and Effect

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Chapter 1 Introduction to the Science of Psychology
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1.6
Three Correlations
The strength and direction of the correlation between variables can be seen in a
graph called a scatterplot. Here are three
examples. In Part A, we have plotted the
cost of a gasoline purchase against the
number of gallons pumped. The number of
gallons is positively and perfectly correlated with their cost, so the scatterplot
appears as a straight line, and you can
predict the value of either variable from a
knowledge of the other. Part B shows a
perfect negative correlation between the
number of miles you have traveled toward
a destination and the distance remaining.
Again, one variable can be exactly predicted from the other. Part C illustrates a
correlation of +.81 between the number
of math courses students had taken in
high school and their scores on a college
math exam; each dot represents one student (Hays, 1981). As correlations decrease, they are represented by less and
less organized scatterplots. A correlation
of .00 would appear as a shapeless cloud.
interpreting what correlations mean. The mere fact that two variables are correlated does
not guarantee that one is causing an effect on the other. And even if one variable actually does cause an effect on the other, a correlation coefficient can’t tell us which variable
is influencing which, or why (see Table 1.6). Consider the question of how aggression
develops. Correlational studies of observational data indicate that children who are in day
care for more than thirty hours a week are more aggressive than those who stay at home
with a parent. Does separation from parents actually cause the heightened aggressiveness
with which it is associated? It might, but psychologists must be careful about jumping to
that conclusion. The most obvious explanation for the relationship found in a correlational study may not always be the correct one. Perhaps the aggressiveness seen among
some children in day care has something to do with the children themselves or with what
happens to them in day care, not just with separation from their parents.
One way psychologists evaluate such alternative hypotheses is to conduct further
correlational studies to look for trends that support or conflict with those hypotheses.
Further analysis of day-care research, for example, shows that the aggressiveness seen
in preschoolers who spend a lot of time in day care is the exception, not the rule. Most
children don’t show any behavior problems, no matter how much time they have spent
in day care. This more general trend suggests that whatever effects separation has, it
may be different for different children in different settings, causing some to express
aggressiveness, others to display fear, and still others to find enjoyment. As described
in the chapter on human development, psychologists are exploring this possibility by
examining correlations between children’s personality traits, qualities of different daycare programs, and reactions to day care (NICHD Early Child Care Research Network,
2005). Throughout this book you will see many more examples of how correlational
studies help to shed light on a wide range of topics in psychology.
Experiments: Exploring Cause and Effect
experiment A situation in which the
researcher manipulates one variable
and observes the effect of that manipulation on another variable, while holding all other variables constant.
independent variable In an experiment, the variable manipulated by the
researcher.
dependent variable In an experiment,
the factor affected by the independent
variable.
Still, to make the best choice among alternative explanations and to confirm cause-andeffect relationships between research variables, psychological scientists prefer to exert
some control over those variables. This kind of controlled research usually takes the
form of an experiment.
In an experiment, the researcher makes a change in one variable and then observes
the effect of that change on another variable, while holding all the other variables constant. The variable that is changed, or manipulated, by the experimenter is called the
independent variable. The variable that is measured following this manipulation is
called the dependent variable, because it depends on the independent variable (see
Table 1.7). So in an experiment on the effects of TV violence, for example, the
independent variable might be the amount of violence that different groups of children
31
Research Methods in Psychology
TA B L E
1.6
Correlation and Causation
Look at the relationships described in the left-hand column, then ask yourself why the two variables in each case are correlated.
Could one variable be causing an effect on the other? If so, which variable is the cause, and how might it exert its effect? Could the
by
relationship between the two variables be caused by a third one? If so, what might that third variable be? We suggest some possible
explanations in the right-hand column. Can you think of others?
doing
2
learn
Correlation
Possible Explanations
A recent survey found that the more sexual content
that U.S. teenagers reported watching on television,
the more likely they were to begin having sex themselves during the following year (Collins et al., 2004).
It might have been some teens’ greater interest in sex that led them to
watch more sexually oriented shows and also to become sexually active.
The number of drownings in the United States rises
and falls during the year, along with the amount of
ice cream sold each month.
This relationship probably reflects a third variable—time of year—that
affects both ice cream consumption and the likelihood of swimming and
boating (Brenner et al., 2001).
In places where beer prices are raised, the number
of new cases of sexually transmitted disease falls
among young people living in those places.
If price increases cause less beer consumption, people might stay sober
enough to remember to use condoms during sexual encounters. The
relationship could also reflect coincidence, because prices do not always
affect alcohol use. More research is required to understand this correlation.
A recent study found that the more antibiotics a
woman has taken, and the longer she has taken
them, the greater is her risk of breast cancer
(Velicer et al., 2004).
Long-term antibiotic use might have impaired the women’s immune
systems, but the cancer risk might also have been increased by the
diseases that were being treated with antibiotic drugs, not the drugs
themselves. Obviously, much more research would be required before
condemning the use of antibiotics.
The U.S. stock market rises during years in which a
team from the National Football Conference wins
the Super Bowl and falls during years in which an
American Conference team wins.
The so-called “Super Bowl Effect” has occurred 30 times in 37 years;
striking as this might seem, coincidence seems to be the most likely
explanation.
TA B L E
doing
2
learn
by
1.7
Independent and Dependent Variables
Fill in the names of the independent and dependent variables in each of these experiments (the answers are listed at the bottom of
page 33). Remember that the independent variable is manipulated by the experimenter. The dependent variable is measured to determine the effect of the independent variable. How did you do on this task?
1. Children’s reading skill is measured after taking either a special
reading class or a standard reading class.
The independent variable is
The dependent variable is
.
.
2. College students’ memory for German vocabulary words is
tested after a normal night’s sleep or a night of no sleep.
The independent variable is
The dependent variable is
.
3. Experiment title: “The effect of a daily walking program on
elderly people’s lung capacity.”
The independent variable is
The dependent variable is
.
4. People’s ability to avoid “accidents” in a driving simulator is
tested before, during, and after talking on a cell phone.
The independent variable is
The dependent variable is
.
.
.
.