Variables: Definition, Types of Variable in Research
Variable Definition in
Research
A variable
is any property, a characteristic, a number, or a quantity that increases or
decreases over time or can take on different values (as opposed to constants,
such as n, that do not vary) in different situations.
When
conducting research, experiments often manipulate variables. For example, an
experimenter might compare the effectiveness of four types of fertilizers.
In this case, the variable is the ‘type of fertilizers’. A
social scientist may examine the possible effect of early marriage on divorce.
Here early
marriage is the variable. A business researcher may find it useful to
include the dividend in determining the share prices. Here dividend is the
variable.
Effectiveness,
divorce and share prices are also variables because they also vary as a result
of manipulating fertilizers, early marriage, and dividends.
Types of Variable
- Qualitative
Variables.
- Quantitative
Variables.
- Discrete
Variable.
- Continuous
Variable.
- Dependent
Variables.
- Independent
Variables.
- Background
Variable.
- Moderating
Variable.
- Extraneous
Variable.
- Intervening
Variable.
- Suppressor
Variable.
Qualitative Variables
An important
distinction between variables is between the qualitative variable and the
quantitative variable.
Qualitative
variables are those that express a qualitative attribute such as hair
color, religion, race, gender, social status, method of payment, and so on. The
values of a qualitative variable do not imply a meaningful numerical ordering.
The value of
the variable ‘religion’ (Muslim, Hindu, ..,etc.) differs qualitatively; no
ordering of religion is implied. Qualitative variables are sometimes referred
to as categorical
variables.
For example,
the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the
values of this variable are expressed in categories, we refer to this as a
categorical variable.
Similarly, place of residence may be categorized as being urban
and rural and thus is a categorical variable.
Categorical
variables may again be described as nominal and ordinal.
Ordinal
variables are those which can be logically ordered or ranked higher or lower
than another but do not necessarily establish a numeric difference between each
category, such as examination grades (A+, A, B+, etc., clothing size (Extra
large, large, medium, small).
Nominal
variables are those who can neither be ranked nor logically ordered, such as
religion, sex, etc.
A
qualitative variable is a characteristic that is not capable of being measured
but can be categorized to possess or not to possess some characteristics.
Quantitative Variables
Quantitative
variables, also called numeric
variables, are those variables that are measured in terms
of numbers. A simple example of a quantitative variable is a person’s age.
The age can
take on different values because a person can be 20 years old, 35 years old,
and so on. Likewise, family size is a quantitative variable, because a family
might be comprised of one, two, three members, and so on.
That is,
each of these properties or characteristics referred to above varies or differs
from one individual to another. Note that these variables are expressed in
numbers, for which we call them quantitative or sometimes numeric variables.
A
quantitative variable is one for which the resulting observations are numeric
and thus possesses a natural ordering or ranking.
Discrete and Continuous Variables
Quantitative
variables are again of two types: discrete and continuous.
Variables
such as some children in a household or number of defective items in a box are
discrete variables since the possible scores are discrete on the scale.
For example,
a household could have three or five children, but not 4.52 children.
Other variables, such as ‘time required to complete an MCQ test’
and ‘waiting time in a queue in front of a bank counter,’ are examples of a
continuous variable.
The time
required in the above examples is a continuous variable, which could be, for
example, 1.65 minutes, or it could be 1.6584795214 minutes.
Of course,
the practicalities of measurement preclude most measured variables from being
continuous.
Discrete Variable
Definition
2.6: A discrete variable, restricted to certain values, usually (but
not necessarily) consists of whole numbers, such as the family size, number of
defective items in a box. They are often the results of enumeration or
counting.
A few more
examples are;
- The
number of accidents in the twelve months.
- The
number of mobile cards sold in a store within seven days.
- The
number of patients admitted to a hospital over a specified period.
- The
number of new branches of a bank opened annually during 2001- 2007.
- The
number of weekly visits made by health personnel in the last 12 months.
Continuous Variable
A continuous
variable is one that may take on an infinite number of intermediate values
along a specified interval. Examples are:
- The
sugar level in the human body;
- Blood
pressure reading;
- Temperature;
- Height
or weight of the human body;
- Rate
of bank interest;
- Internal
rate of return (IRR),
- Earning
ratio (ER);
- Current
ratio (CR)
No matter
how close two observations might be, if the instrument of measurement is
precise enough, a third observation can be found, which will fall between the
first two.
A continuous
variable generally results from measurement and can assume countless values in
the specified range.
Dependent and Independent Variables
In many
research settings, there are two specific classes of variables that need to be
distinguished from one another, independent
variable and dependent
variable.
Many
research studies are aimed at unrevealing and understanding the causes of
underlying phenomena or problems with the ultimate goal of establishing a
causal relationship between them.
Look at the
following statements:
- Low
intake of food causes underweight.
- Smoking
enhances the risk of lung cancer.
- Level
of education influences job satisfaction.
- Advertisement
helps in sales promotion.
- The
drug causes the improvement of a health problem.
- Nursing
intervention causes more rapid recovery.
- Previous
job experiences determine the initial salary.
- Blueberries
slow down aging.
- The
dividend per share determines share prices.
In each of
the above queries, we have two variables: one independent and one dependent. In
the first example, ‘low intake of food’ is believed to have caused the ‘problem
of underweight.’
It is thus the so-called independent variable. Underweight is
the dependent variable because we believe that this ‘problem’ (the problem of
underweight) has been caused by ‘the low intake of food’ (the factor).
Similarly,
smoking, dividend, and advertisement all are independent variables, and lung
cancer, job satisfaction, and sales are dependent variables.
In general,
an independent variable is manipulated by the experimenter or researcher, and
its effects on the dependent variable are measured.
Independent Variable
The variable
that is used to describe or measure the factor that is assumed to cause or at
least to influence the problem or outcome is called an independent variable.
The
definition implies that the experimenter uses the independent variable to
describe or explain the influence or effect of it on the dependent variable.
Variability
in the dependent variable is presumed to depend on variability in the
independent variable.
Depending on
the context, an independent variable is sometimes called a predictor variable,
regressor, controlled variable, manipulated variable, explanatory variable,
exposure variable (as used in reliability theory), risk factor (as used in
medical statistics), feature (as used in machine learning and pattern
recognition) or input variable.
The
explanatory variable is preferred by some authors over the independent variable
when the quantities treated as independent variables may not be statistically
independent or independently manipulable by the researcher.
If the
independent variable is referred to as an explanatory variable, then the term response
variable is preferred by some authors for the dependent variable.
Dependent Variable
The variable
that is used to describe or measure the problem or outcome under study is
called a dependent
variable.
In a causal
relationship, the cause is the independent variable, and the effect is the
dependent variable. If we hypothesize that smoking causes lung cancer,
‘smoking’ is the independent variable and cancer the dependent variable.
A business
researcher may find it useful to include the dividend in determining the share
prices. Here dividend is the independent variable, while the share price is the
dependent variable.
The dependent variable usually is the variable the researcher is
interested in understanding, explaining, or predicting.
In lung
cancer research, it is the carcinoma that is of real interest to the
researcher, not smoking behavior per se. The independent variable is the
presumed cause of, antecedent to, or influence on the dependent variable.
Depending on
the context, a dependent variable is sometimes called a response variable,
regressand, predicted variable, measured variable, explained variable,
experimental variable, responding variable, outcome variable, output variable,
or label.
An explained
variable is preferred by some authors over the dependent variable when the
quantities treated as dependent variables may not be statistically dependent.
If the
dependent variable is referred to as an explained variable, then the term
predictor variable is preferred by some authors for the independent variable.
Levels of an
Independent Variable
If an
experimenter compares an experimental treatment with a control treatment, then
the independent variable (a type of treatment) has two levels: experimental and
control.
If an
experiment were to compare five types of diets, then the independent variables
(types of diet) would have five levels.
In general,
the number of levels of an independent variable is the number of experimental
conditions.
Background Variable
In almost
every study, we collect information such as age, sex, educational attainment,
socioeconomic status, marital status, religion, place of birth, and the like.
These variables are referred to as background
variables.
These
variables are often related to many independent variables so that they
influence the problem indirectly. Hence they are called background variables.
If the
background variables are important to the study, they should be measured.
However, we should try to keep the number of background variables as few as
possible in the interest of the economy.
Moderating Variable
In any
statement of relationships of variables, it is normally hypothesized that in
some way, the independent variable ’causes’ the dependent variable to occur. In
simple relationships, all other variables are extraneous and are ignored. In
actual study situations, such a simple one-to-one relationship needs to be
revised to take other variables into account to better explain the
relationship.
This emphasizes the need to consider a second independent
variable that is expected to have a significant contributory or contingent
effect on the originally stated dependent-independent relationship. Such a
variable is termed a moderating
variable.
Suppose you
are studying the impact of field-based and classroom-based training on the work
performance of the health and family planning workers, you consider the type of
training as the independent variable.
If you are
focusing on the relationship between the age of the trainees and work
performance, you might use ‘type of training’ as a moderating variable.
Extraneous Variable
Most studies
concern the identification of a single independent variable and the measurement
of its effect on the dependent variable.
But still,
several variables might conceivably affect our hypothesized
independent-dependent variable relationship, thereby distorting the study.
These variables are referred to as extraneous
variables.
Extraneous
variables are not necessarily part of the study. They exert a confounding
effect on the dependent-independent relationship and thus need to be eliminated
or controlled for.
An example
may illustrate the concept of extraneous variables. Suppose we are interested
in examining the relationship between the work-status of mothers and
breastfeeding duration.
It is not
unreasonable in this instance to presume that the level of education of mothers
as it influences work-status might have an impact on breastfeeding duration
too.
Education is
treated here as an extraneous variable. In any attempt to eliminate or control
the effect of this variable, we may consider this variable as a confounding variable.
An
appropriate way of dealing with confounding variables is to follow the
stratification procedure, which involves a separate analysis for the different
levels of lies confounding variables.
For this
purpose, one can construct two crosstables: one for illiterate mothers and the
other for literate mothers. If we find a similar association between work
status and duration of breastfeeding in both the groups of mothers, then we
conclude that the educational level of mothers is not a confounding variable.
Intervening Variable
Often an
apparent relationship between two variables is caused by a third variable.
For example, variables X and Y may be highly correlated, but
only because X causes the third variable, Z, which in turn causes Y. In this
case, Z is the intervening
variable.
An
intervening variable theoretically affects the observed phenomena but cannot be
seen, measured, or manipulated directly; its effects can only be inferred from
the effects of the independent and moderating variables on the observed
phenomena.
In the
work-status and breastfeeding relationship, we might view motivation or
counseling as the intervening variable.
Thus,
motive, job satisfaction, responsibility, behavior, justice are some of the
examples of intervening variables.
Suppressor Variable
In many
cases, we have good reasons to believe that the variables of interest have a
relationship within themselves, but our data fail to establish any such
relationship. Some hidden factors may be suppressing the true relationship
between the two original variables.
Such a
factor is referred to as a suppressor
variable because it suppresses the actual relationship
between the other two variables.
The
suppressor variable suppresses the relationship by being positively correlated
with one of the variables in the relationship and negatively correlated with
the other. The true relationship between the two variables will reappear when
the suppressor variable is controlled for.
Thus, for example,
low age may pull education up but income down. In contrast, a high age may pull
income up but education down, effectively canceling out the relationship
between education and income unless age is controlled for.
Concept
The
concept is a name given to a category that organizes observations and
ideas by their possession of common features. As Bulmer succinctly puts it,
concepts are categories for the organization of ideas and observations (Bulmer,
1984:43).
If a concept
is to be employed in quantitative research, it will have to be measured. Once
they are measured, concepts can be in the form of independent or dependent
variables.
In other
words, concepts may explain (explanatory variable) of a certain aspect of the
social world, or they may stand for things we want to explain (dependent
variable).
