Ranjit Kumar - Chapter 5

1. R E S E A R C H M E T H O D O L O G Y
CHAPTER # 5:
“Identifying Variables”
by Muhammad Salman Jamil

2. What is Variable?
 An image, perception or concept that can be
measured – hence capable of taking on
different values- is called a variable.
 If it exists, it can be measured. (Babbie
1989: 105)
 According to Kerlinger, ‘A variable is a
property that takes on different values. A
variable is a symbol to which numerals or
values are attached’ (1986: 27).
 Black and Champion define a variable as
‘rational units of analysis that can assume
any one of a number of designated sets of
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3. Variable verses Concept
 Concepts are mental images or perceptions and
therefore their meaning varies markedly from
individual to individual.
 A concept cannot be measured whereas a variable
can be subjected to measurement by crude/refined or
subjective/objective units of measurement.
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Concept Variable
 Subjected to
measurement
 No uniformity in
understanding among
different people.
Examples:
Effectiveness,
Satisfaction etc.
Measurable though the
degree of precision varies
from scale to scale and
variable to variable.
Examples: Gender, Attitude,
Age etc.
 It is therefore important for the concept to be
converted into variables .

4. Converting concepts into
Variables
 If you are using a concept in your study, you need to
consider its operation - that is, how it will be measured.
 For this, you need to identify indicators- a set of criteria
reflective of the concept which can then be converted
into variables.
 The choice of indicators for a concept might vary with
researchers, but those selected must have a logical link
with the concept.
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VariablesIndicatorsConcept

5.  One of the major difference between qualitative and
quantitative studies is the importance of variables.
 In qualitative research, measurements and variables
don’t significance as the study involves perception,
beliefs or feelings.
 In quantitative studies, the emphasis is on exploring
commonalities in the study population, thus
measurements and variables play an important role.
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6. Concept Indicator Variable Working
Definition
Rich  Income
 Assets
 Income
 Total value
of home, car
or investment
 If Rs >
100000
 If Rs >
250000
Effectivene
ss
 No of
guests
 Changes in
ratings extend
of, pattern of
 No of
excellent per
100 feedback
- Do -
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7. Types of Variable
The variable is classified into broad
categories such as:
1. Casual Relationship
2. Study Design
3. Unit of measurement.
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8. Type of Variable -
Classification
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9. Casual Relationship
 The variable can be classified into multiple broad
categories in casual relationship such as:
1. Change variables, which are responsible for bringing
about change in a phenomenon, situation or
circumstance;
2. outcome variables, which are the effects, impacts or
consequences of a change variable;
3. variables which affect or influence the link between
cause-and-effect variables
4. connecting or linking variables, which in certain
situations are necessary to complete the relationship
between cause-and-effect variables.
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10. Dependent Vs Independent
Variables
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Independent Variable Dependent Variable
 The variable that is
manipulated either by the
researcher or by nature or
circumstance
 Independent variables are
also called “stimulus”
“input” or “predictor”
variables
 Analogous to the “cause”
in a cause-effect
relationship.
 A variable that is
observed or measured,
and that is influenced or
changed by the
independent variable.
 Dependent variables are
also known as “response”
or “output” or “criterion”
variables.
 Analogous to the “effect”
in a cause-effect
relationship.

11.  Extraneous variable – There are several other
factors operating in a real-life situation may affect
changes in the dependent variable. These factors
may increase or decrease the magnitude or strength
of the relationship b/w independent and dependent
variables.
 Intervening variable – It sometimes called the
confounding variable considered as relationship b/w
independent and dependent variables. In certain cases
the relationship cannot be established without the
intervention of another variable. The cause, or
independent variable will have the assumed effect only in
the presence of an intervening variable.
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14. From Point of View of Study
Design
 It relates to the case study experimental based that
determine controlled/contrived experiment but
sometimes it relates to Quasi Experimental or an ex
post facto or non-experimental study.
 In controlled experiments the independent (cause)
variable may be introduced or manipulated either by the
researcher or by someone else who is providing the
service. It has two types of variables are:
 Active Variables
 Attributes Variables
 There are numerous characteristics of research like
age, health beliefs, or weight etc. When they can’t be
manipulated, are attribute variables while other form
termed as active or independent variables.
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16. From Viewpoint of unit measurement
In this viewpoint of unit measurement it is classified into two
categories are: a) Categorical variables b) Continuous
Variables
Categorical Variables: In statistics, a categorical variable is
a variable that can take on one of a limited, and usually fixed,
number of possible values, thus assigning each individual to a
particular group or "category.” it is measure on nominal/ordinal
scale.
 Constant It has single value / Categories
 Dichotomous It takes only two values / Categories
 Polytomous It takes more than two categories such
as Religion, Attitudes etc.
Continuous Variables: A continuous variable can assume
an infinite number of values between two points, it shows the
continuity in measurement like age in days, months & years. It
measures in actual that contained in a range i.e. interval/ ratio
scale. 16

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18. Type of Measurement Scale
 The frame into which we wish to make everything fit
is called Scale. Every scale requires some unit &
characteristics to classify measurements.
 The greater the refinement in the unit of
measurement, the greater confidence in the
measurement of findings.
 There are four measurement scales discovered by a
psychologist researcher named Stanley Stevens:
 Nominal
 Ordinal
 Interval
 Ratio
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19. Nominal Scale – Classificatory Scale
 Nominal scales are used for labeling variables, without any
quantitative values termed as “labels.”
 A good way to remember all of this is that “nominal” sounds a lot
like “name” and nominal scales are kind of like “names” or labels.
 Classification by means of a nominal scale ensures that
individuals, objects or responses within the same subgroup have
a common characteristic or property as the basis of
classification.
 All of these below examples are mutually exclusive (no overlap)
and none of them have any numerical significance.
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20. Ordinal Scale – Ranking Scale
 An ordinal scale has all the
properties/characteristics of a nominal scale, in
addition to its own. Subcategories are arranged in
order of the magnitude of the
property/characteristic.
 We can’t say that it typically measures non-numeric
concepts like satisfaction, happiness, discomfort, etc.
but also deals with concepts like tall/short, far/near etc.
 It is easy to remember because its sounds like “order”.
It’s the order that matters. The best way to
determine central tendency on a set of ordinal data is
to use the mode or median; the mean cannot be
defined from an ordinal set.
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22. Interval Scale
 Interval scales are numeric scales in which we know
not only the order, but also the exact differences
between the values such as rate of change,
temperature Change.
 Time is another good example of an interval scale in
which the increments are known, consistent, and
measurable.
 It is also useful for statistical analysis of data such as
mean, median, mode or standard deviation.
 “Interval” itself means “space in between,” it’s not only
tell us about order, but also about the value of each22

23. Ratio Scale
 A ratio scale has all the properties of Nominal,
Ordinal and Interval Scale. It also has a starting
point fixed at Zero which allows for a wide range of
both descriptive and inferential statistics to be
applied.
 It tells us about the measurement scale in terms of
order determining exact value between units.
 It has benefit of interval data applies to ratio scales
plus ratio scales have a clear definition of zero
such as height and weight.
 It provides wide range of possibilities when it
comes to statistical analysis such as addition,
subtraction, multiplication, division (ratios) also
central tendency can also be calculated from ratio
scales. 23

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25. Prepared By: Muhammad Salman
Jamil 25