In an observational study, researchers collect data in a way that does not
directly interfere with how the data arise.
In other words, they merely observe.
And based on observational studies, we can only establish an association.
In other words, correlation between the explanatory and the response variables.
If an observational study uses data from the past,
it's called a retrospective study.
Whereas if data are collected throughout the study, it's called prospective.
In an experiments on the other hand,
researchers randomly assign subjects to treatments and can, therefore,
establish causal connections between the explanatory and response variables.
Let's pause for
a moment to clarify what we mean by random assignment with an example, suppose we
want to evaluate the relationship between regularly working out and energy level.
We can design this study as an observational study or an experiment.
In an observational study, we sampled two types of people from the population.
Those who choose to work out and those who don't,
then find the average energy level for the two groups of people and compare.
On the other hand, in an experiment, we sample a group of people from
the population, then we randomly assign these people into two groups.
Those who will regularly work out through the course of the stud and
those who will not.
The difference is that the decision of whether to work out or
not is not left up to the subjects as in the observational study, but
is instead imposed by the researcher.
At the end, we compare the average energy levels of the two groups based on
the observational study even if we find the difference between
the average energy levels of these two groups of people,
we can't attribute this difference solely to working out.
Because there may be other variables that we didn't control for
in this study, that contribute to the observed difference.
For example, people who are in better shape might be
more likely to regularly work out and also have higher energy levels.
However, in the experiment, such variables that might also contribute to the outcome
are likely equally represented in the two groups due to the random assignment.
Therefore, if we find a difference between the two averages, we can indeed make
a colossal statement attributing this difference to working out.
Next, we will review media coverage on a public study and
try to determine what type of study it is.
Let's start by reviewing an excerpt from the news article.
Study, breakfast cereal keeps girls slim.
Girls who ate breakfast of any type had a lower average body mass index,
a common obesity gauge than those who said they didn't.
The index was even lower for girls who said, they ate cereal for breakfast.
According to findings of the study conducted by
the Maryland Medical Research Institute with funding from the National Institutes
of Health and cereal-maker General Mills.
The results were gleaned from a larger NIH survey of 2,379 girls in California,
Ohio and Maryland who were tracked between the ages of 9 and 19.
As part of the survey, the girls were asked once a year what
they had eaten during the previous three days.
The title of the article says, breakfast cereal keeps girls slim, but
there actually three possible explanations here.
One, eating breakfast does indeed, cause girls to be slimmer.
Two, being slim might cause girls to eat breakfast, so
the relationship could be reversed.
Three, there may be a third variable that is responsible for
both being slim and eating breakfast.
For example, generally being health conscious might result in being slim as
well as starting the day off with breakfast.
Mine Çetinkaya-RundelAssociate Professor of the Practice
