variables in social science research and its measurement describes the various types of variables in social sciences with examples and the measurement of variables.

1. ODISHA UNIVERSITY OF
AGRICULTURE AND TECHNOLOGY
Presented by : ABHIJEET SATPATHY
1st Year Ph.D.
Department of Extension Education,
College of Agriculture, OUAT, Bhubaneswar
DEPARTMENTAL SEMINAR ON
“USE OF VARIABLES IN SOCIAL
SCIENCE RESEARCH”

2. Topics covered:
Common terms used in social science research
Variables: meaning, definition
Criteria for selection of variables
Types of variables
Measurement of variables
Different scales of measurement

3. Introduction to some common terms used
in social science research
Concept: a concept is a thought, an opinion,
a mental image of a thing formed by
generalization from particular.
Example: weight, Light, temperature, sound,
age, etc. are concepts.
Construct: it is a combination of concepts. A
construct can not be observed directly.
Example: intelligence, learning, hostility,
anxiety
Concept
Construct

4. Fact: logical construct of
concepts. Fact is an
observation which is based on
experience.
Attributes : sub-values of a
variable, such as 'male' and
'female’.
Mutually exclusive attributes
are those that cannot occur
at the same time.
Units are the ways that
variables are classified.
These include: individuals,
groups, social interactions
and objects.

5. What is a variable?
• VARIABLE: “Attribute or quality that could differ in magnitude”
Or
• “A symbol to which numerals or values are assigned.”
Or
• “A variable is something which varies and can have more than one value.”
Example: age, intelligence, income, land holding, risk orientation, innovation
proneness, extent of adoption, etc.
• Constant: Property of objects or things which cannot vary in any situation.
Example: Gender is constant in a study related to women empowerment

6. Criteria for selection of variables:
The variables selected for study should be –
i. According to the objectives of the study.
ii. Mutually exclusive and not overlapping
iii.According to the level of understanding of the researcher.
iv.Measurable by the available techniques or suitable technique could be
developed for the same.
v. Could be classified into some categories.
vi.Limited in number so as to avoid confusion and could be studied with that
resource and time available.
• Variables selected for study shouldbe labelled, such as y for dependent variables
and x1, x2, x3….. Xn for the independent variables

7. Identifying the variables:
Academic
performance
Attitude
towards study
Study habits
Learning
style
Family
background
Self efficacy
and motivation

8. Types of variables in social science research
INDEPENDENT VARIABLE:
It is a variable hypothesized to cause or explain variation in
another variable (i.e., “Influencer”).
Or
The variable from which predictions are made is known as
independent variable. It is the presumed cause of the dependent
variable.
• Example: Age(years), land holding (number of acres of land owned),
income (rupees earned per year) are the independent variable.

9. Dependent variable:
Dependent variable: it is the condition which the researcher
tries to explain, the dependent variable is the “consequent”.
Or
• It is a variable hypothesized to vary depending on the
influence of another variable (i.e., “Consequence”). It is the
variable that is measured.
• Example: Time and extent of adoption, attitude towards
new farm practices, etc.

10. Examples
1. Promotion affects employees’ motivation
Independent variable: promotion
Dependent variable: employees’ motivation
2. A researcher is interested in knowing
“how stress affects mental state of human beings?”
Independent variable: stress
Dependent variable: mental state of human beings
the researcher can directly manipulate the stress levels and can
measure how those stress levels change the mental state.

11. Terminological varieties of dependent and
independent variables
DEPENDENT VARIABLE
Explained
Predictand
Response
Outcome
controlled
Explanatory
Predictor
Stimulus
Covariate
Control
INDEPENDENT VARIABLE

12. Mediating/intervening variable/Hidden variable:
According to Kerlinger, the constructs,
which are non observable , have been
called intervening variables. It can
neither be seen nor heard, nor felt. it
is inferred from the behavior.
Example: Hostility, Anxiety, Fatigue,
Motivation, Learning.

13. Examples:
• Determining the effect of use of audio visual aids on learning ability
of farmers of a village.
The association between audio visual aids and learning ability needs
to be explained
Other variables intervene : such as anxiety, motivation, etc.
• Higher education typically leads to higher income.
Higher education: independent variable
Higher income: dependent income
Better occupation: intervening variable
It is casually affected by education and itself affects income.

14. Quantitative and Qualitative variables
Quantitative variables: The variables which can be measured in
numeric terms. These variables vary in amount. Those
characteristics which may be taken on various magnitudes i.e.,
may exist in greater (or) smaller amounts.
• Example: Age, income, size of land holding, size of group, etc.
• There are 16 students enrolled in P.G 1st year
• These variables are also called as ordered variables because
the difference in the possible values are in ordered degrees.

15. Qualitative Variable
Qualitative variable: this variable is also called attribute (or)
organismic (or) unordered (or) categorical variable. Any
variable that cannot be manipulated (or) at least is difficult to
manipulate is called qualitative variable. They vary in kind,
type.
• Example: education, sex.
• As a student you are a junior or senior.
• Are you a male or female

16. Continuous and Discrete variables
Continuous variable:
• Example: age,
income of
farmers,
adoption
quotients.
values fall along a continuum.
Continuous variables are theoretically,
infinitely divisible into smaller and
smaller fractional units. A continuous
variable is capable of taking an ordered
set of values within a certain range.

17. Discrete variable:
• Example:
• a. the number of
inhabitants in each village.
• b. the number of members
of a university students
union.
Discrete variable is one
which involves counting the
number of events. It
consists of only whole
numbers, fractional values
cannot occur.

18. Stimulus and Response variables:
Stimulus variable: Stimulus variable is the
condition (or) manipulation created by the
researcher so as to evoke a response in an organism.
• Example: method demonstration, a slide show
about a new crop variety, etc.
Response variable (or) Behavioral variable: any
kind of behavior of the respondent is called
behavioral variable. This refers to some action
(or) response of an individual.
Example: Yes or No, True or False
Stimulus
variable
Response
variable

19. Moderator and control variables
Moderator variables: These are special type of
independent variables which are hypothesized to
modify the relationship between the dependent
and independent variables.
• Example: age, intelligence, etc.
Control variables: Those variables which may
effect the relationship between the
independent and dependent variables and
which are ‘controlled’ (effects cancelled out).
Moderator
variable
Control
variable

20. Extraneous variables
Extraneous variable/ nuisance variable: these are the
independent variables which are not related to the
purpose of the study, but which may have a significant
influence upon the dependent variable.
• Example: Intelligence may be one extraneous variable in the training
process through various teaching methods, and the dependent variable
achievement score may be affected by intelligence.

21. Types of extraneous variables:
Subject variables: These are the characteristics of the individuals being studied that
might affect their actions. These variables include age, gender, health status, mood,
background, etc.
Blocking variables or experimental variables: These are characteristics of the persons
conducting the experiment which might influence how a person behaves. Example:
Gender, language.
Situational variables: These are features of the environment in which the study or
research was conducted, which have a bearing on the outcome of the experiment in a
negative way. Example: air temperature, level of activity, lighting, and the time of day.

22. Antecedent variables
• Example: The training, education,
experience of a extension worker are
antecedent variables. Role of
extension worker is independent
variable, and performance of
extension worker is dependent
variable.
Antecedent variable: These
variables occur in terms of
time, prior to independent
variables and are related to
both independent and
dependent variables.

23. Active and Attribute variables
Active variables: a variable
that is manipulated is called
active variable.
• Example: Award of prizes,
giving punishment,
creating anxiety
Attribute variable: variable
that cannot be manipulated
is called attribute variable.
• Example: Sex, race

24. Dichotomous and Polytomous variables
Dichotomous variable: A dichotomous variable is one which may
have only two values.
Example: female-male, agriculturist-non-agriculturist.
Polytomous variable: Polytomous variables have more than two
values and have got many dimensions.
Example: The influencing skill of extension worker may be high,
somewhat high, medium, low, etc.

25. Demographic variables:
“Demographic variables
are characteristics or
attributes of subjects
that are collected to
describe the sample”.
They are also called
sample characteristics.
Demographic
variables cannot
be manipulated.
Example: Some common
demographic variables
are age, gender,
occupation, marital
status, income etc.

26. Organismic variable
• Organismic variables are the internal forces that influence an
organism’s behavior. Or
• Any characteristic of the research participant/ individual under study
that can be used for classification.
Example: mental age, I.Q., Personality. Characteristics, past education,
etc., are examples of organismic variables.

27. Other important variables:
• Contextual variable:
It is an outcome variable that describes a property of a
group , it is constant within the groups and computed from the values
of some other variables that may vary within the group.
Example: the average age in a suburb where a person lives is
a contextual variable.
• Confounding variables:
It is an extra variable that interferes the existing activities.

28. MEASUREMENT OF VARIABLES
• Measurement of variables is quantifying the variables by giving numbers.
• Measurement is defined as assignment of numerals to objects (or) events
according to rules.
Example : measuring the height, weight of man
Types/levels of measurement: quantification of variables according to
mathematical properties, is known as level of measurement.
Levels of measurement:
1. Nominal scale 3. Interval scale
2. Ordinal scale 4. Ratio scale

29. NOMINAL SCALE
• Numbers are assigned to objects or events which can be placed into mutually
exclusive categories.
• Fundamental property is of “equivalence” (=).
• Example: 1. Variable sex has two nominals of classes i.e., Male and female
2. Classification of farmers like big, small and marginal
• Statistical methods used: number of cases, mode, contingency co-efficient,
chi square test, etc.

30. Ordinal scale
• Numbers are assigned to objects or events which can be placed into mutually
exclusive categories and be ordered into greater and less than scale.
• It has no absolute zero point and is also called as ranking measurement.
• Example: Observations may be classified into categories such as taller and
shorter, greater and lesser, faster and slower, harder and easier, and so forth.
• Statistical measures used: co-efficient of correlation, etc.

31. Interval scale
• Numbers are assigned to objects or events which can be categorized,
ordered and assumed to have an equal distance between scale values .
• Intervals between categories are equal but they originate from some
arbitrary point of origin. No meaningful zero point exists.
• Determination of equality of intervals / differences.
• Example: “standard scores” on cognitive & affective scales, température:
fahrenheit & centigrade scales, calendar dates.
• Statistical measures used: mean, standard deviation, f-test, t-test

32. RATIO SCALE
• The ratio level is the same as the interval level with the addition of a
meaningful and non-arbitrary zero point.
• Variables measured at a higher level can always be converted to a lower
level but not vice versa.
• Examples: Years of experience, weight, height, adoption quotient, area,
speed, velocity, temperature: kelvin scale, length, force, etc.