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Though you may think of psychology as a discipline focused on the individual,
most psychologists are interested in understanding people as a whole.
For example, researchers have shown that there’s a correlation
between violent video games and aggressive behavior in children.
But what does that mean?
You may hear someone on the news proclaim,
“Video games are making your child violent!” – but is that a fair conclusion? Let’s take a moment to find out.
To answer this question, we’ll conduct our own simple study.
We’ll recruit 20 children and observe them over the course of one 7-day period.
During that week of observation, we’ll measure how often the children play violent video games
and how aggressive they act.
We’ll define our two variables of interest as follows:
Variable X: the amount of time (in hours) spent playing violent video games.
Variable Y: the number of aggressive acts committed (think of a child pulling someone else’s hair, for example).
Let’s take a look at some hypothetical data.
Here’s the first participant in our study, Lisa.
During the one-week observation period,
Lisa played 2 hours of violent video games and committed 4 aggressive acts.
The very first thing we want to do is plot her data on a scatterplot.
First, let’s draw our X- and Y-axes.
On the X-axis (the horizontal axis), we’ll label it,
“Hours spent playing violent video games.”
Since no one can play less than zero hours, we’ll begin with zero.
Scanning over all the participants,
I see that the most any one child played was 10 hours,
so we’ll go up to 10 on my X-axis.
On the Y-axis (the vertical axis),
again, no one can commit fewer than zero aggressive acts,
so that’s where we’ll start.
The most aggressive acts any one child committed was 20,
so I’ll number up to 20 on my Y-axis.
Now, we can plot Lisa’s data on the graph.
We’ll start at the X-axis and find 2,
since she played 2 hours of violent video games.
Moving over to the Y-axis,
we know she committed 4 aggressive acts.
We’ll draw a point where these two lines intersect.
Lisa has been represented on our scatterplot,
so we’ll move on to the next participant, Jacob.
Jacob played 8 hours of violent videogames
and committed 10 aggressive acts.
Just like before, we’ll find 8 on the X-axis,
and 10 on the Y-axis,
and draw a point where those two lines intersect.
Now let’s fill in the points from the other 18 participants in our study.
As we can see, the points are clustered together fairly closely in a linear fashion.
This indicates there is a correlation between our two variables.
This simply means that there is some relationship
between violent video games and aggression.
But what is the nature of this relationship?
In our example here, we have demonstrated a positive correlation.
That means as one variable increases, the other tends to increase as well.
Here, as number of hours playing violent video games increases,
so do the number of aggressive acts committed.
Stated alternatively, as the number of hours playing violent video games decreases,
so do the number of aggressive acts committed.
This is helpful for the purposes of prediction:
if I know Russell rarely plays violent video games,
it’s reasonable to predict that he commits few aggressive acts.
This is similar to what the actual research study found,
but let’s take a moment to consider what we should conclude if our data looked different.
Here, our data are no longer tightly clustered, but rather spread out.
This indicates that there is no correlation in our data;
that there is no relationship between playing violent videogames and aggression.
Here, if I know Russell rarely plays violent videogames,
that tells me nothing about his aggression.
This, of course, is not actually the case with our present example,
so let’s take a look at a different example.
Here we’ve re-labeled the X-axis, phases of the moon
(specifically, the proportion of the moon’s surface illuminated by the sun)
and our Y-axis, the amount of emergency room visits.
Contrary to the popular notion that full moons bring out the crazy in people,
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thus leading to increased hospital visits,
research suggests that there is no such relationship between these two variables.
That is, knowing whether or not there is a full moon tonight
tells us nothing about the number of patients we could expect to find in the ER --
no prediction should be made.
Let’s look at one final example that, again, is borrowed from an actual research study.
This observational study looked at attractiveness and court fines.
In the study, the authors plotted how fines for misdemeanor convictions (our Y-axis)
varied as a function of defendant attractiveness (our X-axis).
Their data looked like this:
Like before, these data cluster together tightly
and indicate there is a correlation between our two variables.
However, unlike our violent video games and aggression example,
the pattern of clustering is oriented in the opposite direction.
This is an example of a negative correlation.
In a negative correlation, as one variable increases, the other tends to decrease.
In the example here, the more attractive the defendant, the smaller the fine.
To recap, in observational studies such as this, there are three possible outcomes:
Researchers may find there is a positive correlation between two variables.
That means that as X increases, Y tends to increase.
They may find there is a negative correlation between two variables.
That means as X increases, Y tends to decrease.
Finally, there may be no correlation between two variables.
That means there is no relationship between X and Y.