

Research
Methods STA630
VU
Lesson
06
VARIABLES
AND TYPES OF VARIABLES
Variable
is central idea in research.
Simply defined, variable is a
concept that varies. There
are two
types
of concepts: those that
refer to a fixed phenomenon and those
that vary in quantity,
intensity, or
amount
(e.g. amount of education). The second
type of concept and measures of the
concept are
variables.
A variable is defined as anything
that varies or changes in value.
Variables take on two
or
more
values. Because variable represents
a quality that can exhibit
differences in value,
usually
magnitude
or strength, it may be said that a
variable generally is anything
that may assume
different
numerical
or categorical values. Once you
begin to look for them, you
will see variables
everywhere.
For
example gender is a variable; it can
take two values: male or
female. Marital status is a
variable; it
can
take on values of never married,
single, married, divorced, or
widowed. Family income is a
variable;
it can take on values from
zero to billions of Rupees. A person's
attitude toward women
empowerment
is variable; it can range from
highly favorable to highly
unfavorable. In this way
the
variation
can be in quantity, intensity, amount, or
type; the examples can be
production units,
absenteeism,
gender, religion, motivation, grade, and
age. A variable may be
situation specific;
for
example
gender is a variable but if in a
particular situation like a
class of Research Methods if there
are
only
female students, then in
this situation gender will
not be considered as a variable.
Types
of Variable
1.
Continuous and Discontinuous
variables
Variables
have different properties and to
these properties we assign
numerical values. If the values of a
variable
can be divided into
fractions then we call it a
continuous
variable.
Such a variable can
take
infinite
number of values. Income, temperature, age, or a
test score are examples of
continuous
variables.
These variables may take on
values within a given range or, in
some cases, an infinite
set.
Any
variable that has a limited
number of distinct values and which cannot be
divided into fractions, is
a
discontinuous
variable.
Such a variable is also
called as categorical
variable or
classificatory
variable,
or
discrete
variable.
Some variables have only two
values, reflecting the presence or
absence of a
property:
employedunemployed or malefemale have
two values. These variables
are referred to as
dichotomous.
There are others that
can take added categories
such as the demographic variables of
race,
religion.
All
such variables that produce
data that fit into
categories are said to
be
discrete/categorical/classificatory,
since only certain values
are possible. An automotive variable,
for
example,
where "Chevrolet" is assigned a 5 and
"Honda" is assigned a 6, provides no
option for a 5.5
(i.e.
the values cannot be divided into
fractions).
2.
Dependent and Independent
Variables
Researchers
who focus on causal relations
usually begin with an
effect, and then search
for its causes.
The
cause variable, or the one
that identifies forces or
conditions that act on something
else, is the
independent
variable. The
variable that is the effect or is the
result or outcome of another variable is
the
dependent
variable (also
referred to as outcome variable or effect
variable). The independent
variable is
"independent
of" prior causes that
act on it, whereas the
dependent variable "depends on" the
cause.
It
is not always easy to
determine whether a variable is
independent or dependent. Two questions
help
to
identify the independent variable.
First, does it come before
other variable in time?
Second, if the
variables
occur at the same time, does the
researcher suggest that one
variable has an impact on
another
variable?
Independent variables affect or have an
impact on other variables.
When independent
variable
is present, the dependent variable is
also present, and with
each unit of increase in
the
independent
variable, there is an increase or
decrease in the dependent variable also. In
other words, the
variance
in dependent variable is accounted for by
the independent variable. Dependent
variable is also
referred
to as criterion
variable.
19
Research
Methods STA630
VU
In
statistical analysis a variable is
identified by the symbol (X)
for independent variable and by
the
symbol
(Y) for the dependent variable. In the
research vocabulary different
labels have been
associated
with
the independent and dependent variables
like:
Independent
variable
Dependent
variable
Presumed
cause
presumed
effect
Stimulus
Response
Predicted
from ...
Predicted
to ...
Antecedent
Consequence
Manipulated
Measured
outcome
Predictor
Criterion
.
Research
studies indicate that
successful new product
development has an influence on the stock
market
price
of a company. That is, the more
successful the new product turns
out to be, the higher will
be the
stock
market price of that firm.
Therefore, the success
of the
New
product is the
independent
variable, and
stock
market price the
dependent
variable.
The
degree of perceived success of the
new product developed will
explain the variance in the stock
market
price of the company.
It
is important to remember that there
are no preordained variables
waiting to be discovered "out
there"
that
are automatically assigned to be
independent or dependent. It is in fact the
product of the
researcher's
imagination demonstrated
convincingly.
3.
Moderating Variables
A
moderating variable is one
that has a strong contingent
effect
on the independent
variabledependent
variable
relationship. That is, the presence of a
third variable (the
moderating variable) modifies
the
original
relationship between the independent and the dependent
variable.
For
example, a strong relationship has
been observed between the quality of
library facilities (X) and
the
performance
of the students (Y). Although
this relationship is supposed to be
true generally, it is
nevertheless
contingent on the interest and inclination of the
students. It means that only
those students
who
have the interest and inclination to use the
library will show improved performance in
their studies.
In
this relationship interest
and inclination is
moderating variable i.e.
which moderates the strength of
the
association between X and Y
variables.
4.
Intervening Variables
A
basic causal relationship requires
only independent and dependent variable.
A third type of
variable,
the
intervening
variable, appears
in more complex causal relationships.
It comes between the
independent
and dependent variables and shows the
link or mechanism between them. Advances
in
knowledge
depend not only on documenting
cause and effect relationship
but also on specifying
the
mechanisms
that account for the causal
relation. In a sense, the intervening
variable acts as a dependent
variable
with respect to independent
variable and acts as an independent
variable toward the dependent
variable.
A
theory of suicide states
that married people are
less likely to commit
suicide than single people.
The
assumption
is that married people have greater
social integration (e.g. feelings of
belonging to a group
or
family). Hence a major cause of one
type of suicide was that
people lacked a sense of
belonging to
group
(family). Thus this theory
can be restated as a threevariable
relationship: marital
status
(independent
variable) causes the degree of social
integration (intervening variable),
which affects
suicide
(dependent variable). Specifying the
chain of causality makes the
linkages in theory clearer and
helps
a researcher test complex
relationships.
Look
at another finding that fiveday
work week results in higher
productivity. What is the process
of
moving
from the independent variable to the
dependent variable? What exactly is
that factor which
theoretically
affects the observed phenomenon but cannot be seen?
Its effects must be inferred
from the
effects
of independent variable on the dependent
variable. In this workweek hypothesis,
one might
20
Research
Methods STA630
VU
view
the intervening variable to be the job
satisfaction. To rephrase the statement
it could be: the
introduction
of fiveday work week (IV)
will increase job
satisfaction (IVV), which
will lead to higher
productivity
(DV).
5.
Extraneous Variables
An
almost infinite number of extraneous variables
(EV) exist that might
conceivably affect a
given
relationship.
Some can be treated as independent or
moderating variables, but
most must either be
assumed
or excluded from the study.
Such variables have to be identified by
the researcher. In order to
identify
the true relationship between the
independent and the dependent variable, the
effect of the
extraneous
variables may have to be controlled.
This is necessary if we are
conducting an experiment
where
the effect of the confounding factors has
to be controlled. Confounding factors is another
name
used
for extraneous variables.
Relationship
among Variables
Once
the variables relevant to the topic of
research have been identified,
then the researcher is interested
in
the relationship among them. A statement
containing the variable is called a
proposition. It may
contain
one or more than one variable. The
proposition having one variable in it
may be called as
univariate
proposition, those with two
variables as bivariate proposition,
and then of course
multivariate
containing
three or more variables. Prior to the
formulation of a proposition the
researcher has to
develop
strong logical arguments which
could help in establishing the
relationship. For example,
age at
marriage
and education are the two
variables that could lead to
a proposition: the higher the
education,
the
higher the age at marriage. What
could be the logic to reach
this conclusion? All relationships
have
to
be explained with strong logical
arguments.
If
the relationship refers to an observable reality,
then the proposition can be
put to test, and any
testable
proposition
is hypothesis.
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