Variables: Independent, Dependent, Intervening
As in all scientific research, the goal of research in sociology and criminal justice is to establish
causal relationships between variables. In these endeavors, one variable is hypothesized to be
the cause and the second variable is the effect. The term for the cause is the INDEPENDENT
variable; the term for the effect is the DEPENDENT variable. [Actually, this statement is an
oversimplification of the definitions of these variables, as well as the complexities of their
relationships. However, for the purposes of this course, we will treat these variables as they are
presented here.]
Once we have a basic understanding of independent and dependent variables, a question arises:
in a research hypothesis, how do we tell which is the independent variable and which is the
dependent variable. Actually, there is a relatively simple way to distinguish them: The
independent variable occurs prior in time to the dependent variable. Consider this example.
Hypothesis: The average annual income of men is greater than the average income of women.
It should be clear that the 2 variables in this hypothesis are gender (men and women) and
average annual income. Which of these 2 variables comes first in time? Does a person earn an
annual income and then become a man or woman? Or is a person a man or woman first and
then earn an annual income? It should be obvious that gender precedes average annual
income; hence gender is the independent variable and average annual income is the dependent
variable.
Note that, in this example, the independent variable- gender- comes first in time, but not first in
the sentence. The dependent variable- average annual income- is presented first and is
followed by the independent variable. Not infrequently, the sentence is presented with the
independent variable first, followed by the dependent variable. The important point is that the
independent variable is not necessarily the first one stated in the hypothesis, but it is the first
one in time.
Though not typically stated, many social science hypotheses begin with an implied expression:
“Other things being equal”. In the current example, we could re-phrase the hypothesis as:
“Other things being equal, the average annual income of men is greater than the average
income of women”. Stated this way, the hypothesis acknowledges that there may be additional
variables which might have an effect on the proposed relationship between the independent
and dependent variables. These additional variables are often termed intervening variables
because they come between the independent and dependent variables and may modify the
relationship between them. In our hypothesized association between gender and average
annual income, here are 3 possible intervening variables.
1) Job category: Many women are (still) employed in clerical positions, while men are more
often in managerial positions;
2) Education: Male employees may have higher levels of education than females;
3) Seniority: Women may have had to remain out of the work force to raise children, thus
delaying their entry into employment; men are less likely to have to do this.
These are not the only intervening variables which might effect the relationship between gender
and average annual income, but they do point to the necessity to acknowledge them and
incorporate some measure(s) of them in the research design.
Here is how we might diagram the relationships between these variables in our hypothesis.
Independent Variable
Intervening Variable(s)
Dependent Variable
Male
Job Category- Clerical
Average Annual Income
Female
Job Category- Clerical
Average Annual Income
Male
Job Category- Managerial
Average Annual Income
Female
Job Category- Managerial
Average Annual Income
Male
Education- College Degree
Average Annual Income
Female
Education- College Degree
Average Annual Income
Male
Education- Less than
College
Average Annual Income
Female
Education- Less than
College
Average Annual Income
Male
Seniority- More than x
Years
Average Annual Income
Female
Seniority- More than x
Years
Average Annual Income
Male
Seniority- Less than x
Years
Average Annual Income
Female
Seniority- Less than x
Years
Average Annual Income
The first 4 rows of this table allow us to compare
the average annual incomes of males and females
in clerical and managerial job categories. If male
workers in both job categories earn higher average
annual incomes than female workers, this confirms
the original hypothesis.
The next 4 rows compare the average annual incomes
of male and female employees who are college
graduates or not college grads. If male workers at
both educational levels earn higher average annual
incomes than female workers, this confirms the
original hypothesis.
The last 4 rows compare the average annual incomes
of male and female employees who have x years of
work experience (x is a number which can be derived
from a literature review) and those who have less
experience. If male workers at both seniority levels
earn higher average incomes than females at the
same levels, this confirms the original hypothesis.
It should be pointed out that, if any of these comparisons show that there is no difference in average annual incomes between the genders,
or that female workers earn higher average annual incomes than their male counterparts, further research is necessary to clarify the
relationships between these variables.