This headless professor video
is about confounding variables.
Sometimes they're called missing
variables,
lurking variables, extraneous variables,
third variables, or hidden variables.
This is the kind of
variable
that is a potential cause of the
dependent variable, and that has
a potential influence that we really
don't know
what kind of a variable is
because this variable was
one that was not held constant,
it was not randomized, it was not manipulated
in the experiment, and it
was not even measured.
The problem with
these confounding variables is that they
impair our ability to make
causal inferences.
They do not impact our ability
to use prediction, just to figure out
cause and effect relationships.
Let's look at some
examples.
of confounding variables.
Let's suppose you study infant
circumcision
as an independent variable and
the rate sexually transmitted diseases
as the dependent variable. A hundred years ago,
in Europe, it was noticed that there was a
correlation between these two variables.
The assumption was that if we simply
circumcised more
males we could reduce the rate of
sexually transmitted disease.
The problem is
there were various compounding variables,
such as religion,
ethnicity, and social class because it
was largely
Jewish men that were being circumcised
as infants, and so we were really
dealing with
a comparison of some of the norms
of sexual behavior in Jewish culture.
Let's take a look at some other examples of confounding variables.
I have seen studies
that correlated this school attended
and the SAT score per student.
Well, there does seem to be
some relationship between these two
but it's only because the school attended  has something to do
with some of these confounding variables,
like the education level of the
parents,
the wealth of the parents,
the entire geography of the neighborhood
in which the school was located.
I spent a lot of time in the last twenty
years
working at variables like birth order
and their relationship with personality,
and I've seen
all kinds of correlations. Birth order can  be related to
other variables, such as family size
and family size could be influenced by religion.
Religious people than have more
children.
Ethnicity,  certain ethnic groups have
more children.
Social Class, so all these factors
could count for the correlations
between birth order and personality.
I have also done a number of studies on
handedness
trying to figure out: do left-handed
people differ from right-handed.
We can certainly find correlations, but
there could be confounding
variables, maybe genetics.
Is it really the same gene
that creates handedness
that causes people to have certain
different personality traits?
or could it simply be other genes
associated with those
that actually result in those
personality traits?
Let's look at one specific example
of a research study and try to see
the many confounding variables that
would exist under different kinds of
research designs.
Suppose you're interested in studying
China's
one-child policy and how that might be
related to the
personality "only children"?
Well, what are we going to do for
research design
that will help us figure out
these personality factors influenced by the one-child policy?
Are we going to compare majority of
Chinese families that
have only one child with those are
the
scofflaws that have more than one child?
Well, unfortunately there are other
confounding variables
separating the scofflaws from those who
adhere to the one-child policy.
For example there's religious
differences.
The people who tend to have more than
one child tend to be
more religious. Geography is another factor.
Urban dwellers have fewer children versus those living in western provinces.
There will be differences in
ethnicity the Han Chinese
versus the Uighurs, for example, and
also differences in wealth.
Well of course we could also compare
people on the Chinese mainland with the
immigrant Chinese in other countries
such as Singapore or Taiwan or
Canada or the United States.
Unfortunately, that would be adding other confounding variables such as
the new culture or economic system
that the Chinese immigrants find
themselves in.
And also remember that immigration
always remains a selective process.
Immigrants are not a random
sample from the host country.
These are individuals who chose to go
abroad.
Of course we can compare present  Chinese children
with data on personality traits from
pre-1970 when the one-child
policy was adopted.
But, there have been a lot of other changes in China since then
especially changes in economics.
Well, if we want a solution to the
problem of confounding variables,
the best solution is a larger sample.
With a large sample size we can do
better
randomization of these variables.
Also when it comes to experiments we can do
factorial designs have more than two
variables that were going to manipulate
And for doing a survey. we can enhance
that survey with
structural equations so that we can measure
these other possible confounding
variables.
If we don't have a large N, the best thing we can probably do
is to control the homogeneity
of the sample so that we're looking at one
particular, well-defined population
and that will actually work well to
prevent these other confounding
variables and
limit the degree to which we can
generalize our findings
but that's the best we can do with
a small sample size.
Well, here are some suggestions about
how to overcome confounding variables.
