Variables in Social Research

1. Course Instructor
Dr. Rupasi Tiwari
Pr. Scientist and Incharge ATIC
ICAR-Indian Veterinary Research Institute
Izatnagar, UP-243122, India

2. Construct, Concept & Variables
Concepts are defined as abstract ideas. They are understood to be the fundamental building blocks
of the concept behind principles, thoughts and beliefs. It's a general idea about a thing or group of
things, derived from specific instances or occurrences. It often applies to a theoretical idea in
science: Einstein's contribution to the concept of relativity.
A construct is an abstract concept that is specifically chosen (or “created”) to explain a given
phenomenon.
A construct may be a simple concept, such as a person’s weight, or a combination of a set of
related concepts such as a person’s communication skill, which may consist of several underlying
concepts such as the person’s vocabulary, syntax, and spelling.
The former instance (weight) is a unidimensional construct, while the latter (communication skill) is
a multi-dimensional construct (i.e., it consists of multiple underlying concepts).
The distinction between constructs and concepts are clearer in multi-dimensional constructs,
where the higher order abstraction is called a construct and the lower order abstractions are called
concepts.
However, this distinction tends to blur in the case of unidimensional constructs.

3. Construct, Concept & Variables
Variables are created by developing the construct into a measurable form.
Variables, by definition, correspond to any characteristic that varies (meaning
they have at least two possible values). Examples of variables include height
in inches, scores on a depression inventory, and ages of employees.

4. What is a variable
•A variables is a symbol to which numerals or values are assigned.
•It is a property that takes on different values.
•In fact, variable is something that varies.
•For instance X is a variable. It is a symbol to which we assign numerical
values.
•For example, Sex, education, attitude, aspiration, achievement,
motivation, mass- media exposure etc. In other words variable is a set of
values that form a classification.
• Any item which can be observed or measured and whose observation or
measurement will be useful for the study can be regarded as a variable
for the study.
•For instance, age can be considered a variable because age can take on
different values for different people or for the same person at different
times.
•Similarly, country can be considered a variable because a person's
country can be assigned a value.

5. What is a variable
•Variables aren't always 'quantitative' or numerical.
•The variable 'gender' consists of two text values: 'male' and 'female'. We
can, if it is useful, assign quantitative values instead of (or in place of) the
text values, but we don't have to assign numbers in order for something to
be a variable.
• Some variables can be quite concrete such as gender, birth order, weight,
or shoe size.
•Others can be considerably more abstract, vague, and squishy. For example,
sense of well being, self-esteem, strength of belief in religion, or IQ.
•Basically, variables are the things about people that we can say one person
has more of than another. So we find that people vary in their gender and
shoe size, and their self-esteem and their IQ.

6. Attributes: Sub Variables
The variables can be made up of a number of sub variables
which are called Attributes.
An attribute is a specific value on a variable.
For instance, the variable sex or gender has two
attributes: male and female. Or, the variable agreement might
be defined as having five attributes: Strongly disagree, Disagree,
Neutral, Agree and Strongly agree

7. Traits of Sub Variables/Attributes
2 Traits-
1. Attributes should be exhaustive:
 It should include all possible answerable responses.
 For instance, if the variable is "religion" and the only options are "Hindu’s",
"Sikh", and "Muslim", there are quite a few religions that haven't been
included. The list does not exhaust all possibilities.
 On the other hand, if you exhaust all the possibilities with some more
variables you would simply have too many responses.
 The way to deal with this is to explicitly list the most common attributes
and then use a general category like "Other" to account for all remaining
ones.

8. Traits of Variable
The attributes of a variable should be mutually exclusive
• No respondent should be able to have two attributes
simultaneously .
•Thus in a survey a person may be requested to select one
answer from a list of alternatives (as opposed to selecting as
many that might apply).
•Example: Caste: General/OBC/SC/ST
•Landholding: Large/Medium/Small/Marginal/Landless

9. OperationalDefinitionOfConceptualVariable
•Sometime variable are selected for study which are abstract or conceptual.
•To make these variables understandable & measurable for empirical study
its necessary to operationally define & explain them.
• e.g:- supe (1969) for his p.hd study on rationality in farmers decision
making. Operationally define that behaviour of a farmer is considered
rational if its oriented towards maximisation of profit.

10. OperationalDefinitionOfConceptualVariable
•He further explained that the action of a farmer is rational to the extent to
which he justifies his selection of most efficient mean from among the
available alternatives on the basis of scientific criteria for achieving
maximum economic profit. Rationality was quantified by using a 3 points.
Scale :-rational(score 3)
intermediate (score 2)
less rational (score 1)

11. TYPESOFVARIABLES

12. QUALITAIVE& QUANTITATIVE
VARIABLE
1. Qualitative
Any variable that cannot be manipulated or at least is difficult to manipulate is called
qualitative variable.
The qualitative variable refer to quality or characteristics or attribute and hence
known as attribute variable also.
All variable that include human characteristic such as colour, race, sex, religion,
education, adoption or attitude are qualitative variables.
They are descriptive in nature. Since qualitative characters cannot be ordered in
magnitude; their precise measurements are not possible.
However, we may obtain frequencies (a qualitative variable), corresponding to
different categories of opinion (a qualitative character), by assigning values in order.
These variable are also known as organismic variable variables.
These include attribute variables, unordered variable or categorical variables.

13. 2. Quantitative
The quantitative variable refers to those variables which are composed of categories
that can be ordered in magnitude i.e. it may exist in greater or smaller amounts.
These are theoretically infinitely divisible into smaller and smaller fractional units.
Examples of quantitative variables are height, age, crop yield, GPA, salary,
temperature, area, air pollution index (measured in parts per million), income, herd
size, length of experience in the cultivation of particular crop, experience in dairy
farming, adoption quotient, land holding, and other numerous characteristics.
With the quantitative variables, precise measurements are possible because they
can easily be ordered in terms of increasing or decreasing magnitude.
A quantitative variable is naturally measured as a number for which meaningful
arithmetic operations make sense.

14. Discreteandcontinuousvariables
Discrete Variable
Quantitative variables can be further divided into two categories – continuous
variable and discrete variables.
A discrete variable is one which involves counting the numbers of events.
The numbers of self help groups, the number of inhabitants in each village are
some of the example of discrete variable.
Discrete variable is one which can take only certain values and none in
between.
These are not capable of being measured in any arbitrary degree of fineness
or exactness because the variables contain a clear gap.
For example, the number of members in a group may be 10, 15 or 20 etc. A
discrete variable consists only of whole numbers, fractional values such as
10.5 15.5 or 20.5 cannot occur.
The number of girls in a class or school, the number of computers in a library
and so on are some examples of discrete variables.

15. Discreteandcontinuousvariables
Continuous Variable
A continuous variable is one which is capable of being
measured in any arbitrary degree of fineness or exactness.
Age, height, intelligence, income, Adoption quotient etc, are
some examples of continuous variable.
Such variable can be measured in the smallest degree of
fineness e.g. height:- take any value within some range.
Its value is not fixed (e.g. 1.52-1.525—1.595-1.600).

16. NominalvariablesVsOrdinalVariables
Nominal variables allow for only qualitative classification.
That is, they can be measured only in terms of whether the individual items
belong to certain distinct categories, but we cannot quantify or even rank
order the categories:
Nominal data has no order, and the assignment of numbers to categories is
purely arbitrary. Because of lack of order or equal intervals, one cannot
perform arithmetic (+, -, /, *) or logical operations (>, <, =) on the nominal
data.
Typical examples of such variables are: Gender (Male, Female) or Marital
status (Unmarried, Married, Divorcee)

17. Ordinalvariable
•A ordinal variable is a nominal variable, but its different states are ordered in a
meaningful sequence.
•Ordinal data has order, but the intervals between scale points may be uneven.
• Because of lack of equal distances, arithmetic operations are impossible, but logical
operations can be performed on the ordinal data.
•A typical example of an ordinal variable is the socio-economic status of families.
•We know 'upper middle' is higher than 'middle' but we cannot say 'how much higher'.
•Ordinal variables are quite useful for subjective assessment of 'quality; importance or
relevance'.
•Ordinal scale data are very frequently used in social and behavioral research.
• Almost all opinion surveys today request answers on three-, five-, or seven- point
scales.
• Such data are not appropriate for analysis by classical techniques, because the
numbers are comparable only in terms of relative magnitude, not actual magnitude.

18. Ordinalvariable
Consider for example a questionnaire item on the time involvement
of scientists in the extension activities. The respondents were asked
to indicate their involvement by selecting one of the following codes:
1 = Very low or nil
2 = Low
3 = Medium
4 = High
5 = Very High
Here, the variable 'Time Involvement' is an ordinal variable with 5
states.

19. DummyVariablesfromQuantitative
Variables
A quantitative variable can be transformed
into a categorical variable, called a dummy
variable by recoding the values.
For example: the quantitative variable years
of experience in dairy farming can be
classified into five intervals.
The values of the associated categorical
variable, called dummy variables, are 1,
2,3,4,5:
Years of Experience
Dummy
Variables
[Up to 25] 1
[25, 40 ] 2
[40, 50] 3
[50, 60] 4
[Above 60] 5

20. Preference Variables
Preference variables are specific discrete variables, whose values are either in a decreasing or
increasing order.
For example, in a survey, a respondent may be asked to indicate the importance of the
following nine sources of information for his farm operations, by using the code 1 for the
most important source and 9 for the least important source:
Farm Literature published in the country
Farm Literature published abroad
Television
Radio
Discussions with friends & neighbours
Discussions with scientist
Discussions with successful farmers
Exhibition & farmers fairs
Newpapers
The preference data are also ordinal. The interval distance from the first preference to the
second preference is not the same as, for example, from the sixth to the seventh preference.

21. Multiple Response Variables
Multiple response variables are those, which can assume more than one
value.
A typical example is a survey questionnaire about the type of plants fed
to goats by farmers.
The respondents were asked to indicate the type of plants/trees fed to
goats in ravines area.
The respondents could indicate more than one category.
Acacia leucophloea
Zizyphus Mauritania
Azadirachta indica
Capparis decidua
Prosopis juliflora
Cajanus cajan

22. Interval - scale Variables Vs Ratio Scale
Variables
Interval scale data has order and equal intervals.
Interval scale variables are measured on a linear scale, and can take on
positive or negative values.
It is assumed that the intervals keep the same importance throughout the
scale.
They allow us not only to rank order the items that are measured but also
to quantify and compare the magnitudes of differences between them.
We can say that the temperature of 40°C is higher than 30°C, and an
increase from 20°C to 40°C is twice as much as the increase from 30°C to
40°C.
Almost no variables used in social science are interval-level variables, with
the exception of time measured in calendar years

23. Interval - scale Variables Vs Ratio Scale
Variables
Ratio - scale Variables are continuous positive measurements on
a nonlinear scale.
A typical example is the body weight of an individual in year n.
Example the body weight of individuals was 20 kg in year n than
in year n1 it became 40 kg. So we can say that the individual X’s
body weight has just doubled in n1 year.
Ratio data are also interval data, but they are not measured on
a linear scale.

24. Interval - scale Variables Vs Ratio Scale
Variables
With interval data, one can perform logical operations, add, and subtract,
but one cannot multiply or divide.
For instance, if a liquid is at 40 degrees and we add 10 degrees, it will be 50
degrees. However, a liquid at 40 degrees does not have twice the
temperature of a liquid at 20 degrees because 0 degrees does not represent
"no temperature" -- to multiply or divide in this way we would have to use
the Kelvin temperature scale, with a true zero point (0 degrees Kelvin = -
273.15 degrees Celsius).
In social sciences, the issue of "true zero" rarely arises, but one should be
aware of the statistical issues involved.
Various ratio scale variables are height, distance, land holding, income etc.

25. Extraneousvariable
These are those independent variable which are not related to the objectives of
the study, but may directly or indirectly affect the dependent variables.
For example a research study wants to asses the affect of a video film on gain in
knowledge of the respondents.
But, there may be some other variables like the information sources such as radio
or newspaper or the individuals own intelligence that might also put an effect on
the dependent variable “Gain in knowledge”.
The researcher find out that trainees with high intelligence tend to score high on
the knowledge test and those who are low on intelligence score are low on
knowledge test.
Thus, the variable, intelligence (not of direct interest to the investigator) needs to
be controlled because it is a source of variance, which may influence the gain in
knowledge scores.
Since the variable intelligence effect has not been included in the study, therefore
for this study it may be called extraneous variables.

26. Extraneousvariable
The effect of extraneous variables on the dependent variable is technically called
“experimental error”.
Every research study should strictly be designed in a manner to reduce the effect of
extraneous variables on the dependent variable.
For the above study this may be done by selecting individuals with same intelligence
level or the individuals with differing intelligence may be divided into various groups
and then the experiment may be done to minimize the experimental error.

27. Active variable
•A variable that is manipulated is called active variable.
•When one uses different methods of extension teaching.
•For instance, for awarding prizes for those farmer discussion group which
function efficiently or stoppage of subsidy of stamp charge for ineffective
discussion groups, creating anxiety through agricultural film shows, one is
actively manipulating the variable of reward and punishment and anxiety.
•Thus award of prizes, giving punishment, creating anxiety are examples of
active variables.
•Providing training to change the knowledge & skills of individual is the
example of active variable.

28. Moderatorvariable andcontrol
variable
• The moderator variables are special type of independent variables which are
hypothesized to modify the relationship between independent and dependent
variables. Age, intelligence, etc. are examples of moderator variables.
• Control variable are those which may affect the relationship between the
independent and dependent variables, and which are controlled (effects cancelled
out) by eliminating the variable, holding the variable constant or using statistical
methods.
•The difference between a control and a moderator variable is that the effects of the
control variable are minimized, eliminated or held constant while the effects of the
moderator variables are studied.
•Since both control and moderator variables are independent variables, it is up to
the researcher to determine the independent, moderator and control variables.

29. DichotomousVs. Polytomous Variable
Dichotomous variable is one which have only two values.
For example of two valued variables are male and female,
agriculturalist and non- agriculturalist, viewers and non-viewers
of television, adopter and non-adopters, beneficiaries & non
beneficiaries etc.
Polytomous variable have got many dimension for instance,
skills of extension personnel.
It may be high, some what high, medium or low etc.

30. IndependentVariable Vs Dependent
Variable
•The variable that is predicted or for which predictions are made is called the
dependent variable.
•The dependent variable is the consequent.
•The dependent variable is the condition which the researcher tries to explain.
•The independent variable is the presumed cause of the dependent variable,
(the presumed effect).
•The independent variable is the antecedent & dependent variable the
consequent.
• In one sense every variable is a dependent variable until otherwise lined up
to be the Independent variable for a particular research question.

31. IndependentVariableVsDependentVariable
• The independent variable is what we are studying with respect to how it is related to or
influences the dependent variable.
•If the independent variable is related to or influences the dependent variable, it can be used to
predict the dependent variable.
•It is therefore sometimes called the predictor variable, or the explanatory variable.
•The independent variable may be manipulated or it may just be measured.
• In contrast, the dependent variable is what we are studying, with respect to how it is related to
or influenced by the independent variable or how it can be explained or predicted by the
independent variable.
•It is sometimes called the response variable or the criterion variable.
•It is never manipulated as a part of the study. Dependent variable are the things we measure
about people.

32. Forexample:
A researcher wish to study the effect of training on the knowledge level
of the trainees then here the training is the independent & the
knowledge the dependent variables.
If the researcher now wish to see that whether respondents who have
higher knowledge with respect to scientific dairy farm, also have higher
production levels in their farm then in this case the knowledge level
become independent & farm production the dependent variables.
From this example, it should be clear that the distinction between the
independent and dependent variable is based not on manipulation but
on the questions one is asking of the data.

33. Example Contd:
A useful hint for determining which variable is which in a study is to ask
whether you are trying to either influence or predict one variable from some
other variable or variables.
If so, that variable is probably the dependent variable. The variable that you
are using to make the predictions or to determine if it influences (rather
than is influenced by) some other variable in the study is typically the
independent variable.
For this reason, the independent variable is sometimes called the
"explanatory" variable while the dependent variable is sometimes called the
"response" variable.
You try to "explain" variation in responses on the dependent variable with
the independent or "explanatory" variable(s).

34. InterveningVariable
According to Kerlinger, the constructs which are non-observable have been
called Intervening variable.
Intervening variable is a term invented to account for internal and directly
unobservable, psychological process that in turn account for behaviour.
An intervening variable is an in the head variable.
It can neither be seen nor heard nor felt.
It is inferred from the behaviour.
Learning is inferred from among other things, increases in test scores.
An intervening variable is an unobservable entity. Some scientist have
considered “motivation” as something pressured to be inside the individual
something prompting him to behave in a particular manner.

35. Stimulus- ResponseVariable
A stimulus variable is the condition of manipulation
created by the researcher so as evoke response in an
organism.
The general classes of things the researchers observe that
relate to the environment, situation or condition of
stimulation are referred to as stimulus variables.
A stimulus variable in extension research may be items like
a film show about a new animal, a study tour, method
demonstration of crystoscope for determing heat in
animals etc.

36. Stimulus- Response Variable
•A response variable refers to some action or response of an individual .
•It is any kind of behaviour of the respondent.
•It may also refer to the frequency with which a particular event occurs or it
may be the scale value of a particular event.
•At one extreme these action may consist of relatively simple response such
as yes or no or true or false for a particular question.
•The responses of farmers for queries like, What is your opinion about the
training? (Very good, Good, Average, poor) or Your perception on the impact
of gosthi (Useful, not useful), are examples of response or behavioural
variables.

37. CRITERIAFORSELECTIONOFVARIABLE
The variable selected for study should be :-
1. According to the objectives of study or related to the hypothesis
formulated.
2. Mutually exclusive & not overlapping.
3. According to level of understanding of the researcher.
4. Measured by the available techniques or a suitable techniqe could
be developed for the same.
5. If required could be classified into some categories or arranged
into a model .
6. Limited in number so as to avoid confusion & could be studied with
the resources & time available.

38. ROLE OF VARIABLES IN RESEARCH
•It simplify the research process by combining particular characteristics
objects or people into more general category.
•Eg-technologically, advanced families –such as VCR, CD player, computer.
• Researcher use variables to organise their observations into meaningful
summaries & to transmit this information to the research community.
• Variables play a significant role in theory building eg- memory trace theory,
frustration – aggression theory.