Defining Variables
Variables can be defined as any aspect of a theory
that can vary or change as part of the interaction
within the theory.
In other words, variables are anything can
effect or change the results of a study.
Every study has variables as these are needed
in order to understand differences.
In our theory, we have proposed that students
exposed to the workforce take a more active role in
their education than those who have no exposure.
Looking at this theory, you might see that
several obvious variables are at play, including
‘prior work experience’ and ‘age of
student.’
However, other variables may also play a role
in or influence what we observed.
It is possible that older students have
better social skills causing them to interact more
in the classroom.
They may have learned better studying skills,
resulting in higher examination grades.
They may feel awkward in a classroom of
younger students or doubt their ability more and
therefore try harder to succeed.
All of these potential explanations or
variables need to be addressed for the results of
research to be valid.
Let’s
start with the variables that are directly related
to the theory.
First, the prior work experience is what we
are saying has the effect on the classroom
performance.
We could say that work history is therefore
the cause and classroom grades are the effect.
In this example, our independent variable
(IV), the variable we start with (the input
variable) is work experience. Our
dependent variable (DV), or the variable we end up
with (the outcome variable) is grades.
We
could add additional variables to our list to create
more complex research.
If we also looked at the affect of study
skills on grades, study skills would become a second
independent variable.
If we wanted to measure the length of time to
graduation along with grades, this would become a
second dependent variable.
There is no limit to the number of variables
that can be measured, although the more variables,
the more complex the study and the more complex the
statistical analysis.
The
most powerful benefit of increasing our variables,
however, is control.
If we suspect something might impact our
outcome, we need to either include it as a variable
or hold it constant between all groups.
If we find a variable that we did not include
or hold constant to have an impact on our outcome,
the study is said to be confounded.
Variables that can confound our results,
called confounding variables, are categorized into
two groups: extraneous and intervening.
Extraneous Variables.
Extraneous variables can be defined as any
variable other than the independent variable that
could cause a change in the dependent variable.
In our study we might realize that age could
play a role in our outcome, as could family history,
education of parents or partner, interest in the
class topic, or even time of day, preference for the
instructor’s teaching style or personality.
The list, unfortunately, could be quite long
and must be dealt with in order to increase the
probability of reaching valid and reliable results.
Intervening Variables.
Intervening variables, like extraneous
variables, can alter the results of our research.
These variables, however, are much more
difficult to control for.
Intervening variables include motivation,
tiredness, boredom, and any other factor that arises
during the course of research.
For example,
if one group becomes bored with
their role in the research more so than the other
group, the results may have less to do with our
independent variable, and more to do with the
boredom of our subjects.
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