Variables, hypothesis etc zoom meet

Deprtment of Education, MSU
Patron

Organizing Secretary

Variables, Hypothesis, Sampling Techniques
and Research Tools
A. Veliappan, Ph.D
Assistant Professor
Department of Education
Manonmaniam Sundaranar University
Tirunelveli-627 012

Research
• What is Variable?- Types
• What is Hypothesis? Types
• How to make the sampling
process? Various types
• What is research tools? - types

• The universe contains unknown facts.
• Through the learning process the man is
searching the unknown facts. This searching
process is known as ‘Research’.
• Research means, search for knowledge.
• Knowing the unknown facts from the universe.

• Research is base for development of a nation.
• Today’s progress is based on yesterday’s
research.
• Continuous research in any field provides
fruitful aspects for the future.
• Government and NGO sector
• Business sector
• Social sector

Variables
• If a characteristic of an observation (participant) is the
same for every member of the group i.e. it does not
vary, it is called a constant.
• If it is differs for group members it is called a variable.
• Variable: is a concept or abstract idea that can be
described in measurable terms. E.g qualities, traits, or
attributes
• Anything that can vary can be considered a variable.
E.g age, Income.
• A variable is not only we measure, but also we can
manipulate and something we can control for.Deprtment of Education, MSU

Agenda
 Control/Constant variable
 Independent variable
 Dependent variable
 Intervening/Mediating variable
 Quantitative and Qualitative variables
 Continuous and Discontinuous variables
 Extraneous variable

E.g Intelligence, Mental health, Motivation etc..

E.g Achievement test score

- Stimulus
- Control
- Response
- Controlled

Example 2
(E.g) 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 YDeprtment of Education, MSU

Quantitative and Qualitative Variables
 Quantitative variables: Interval, and ratio variables are
quantitative. It is also called continuous variables
because they have a variety (continuum) of
characteristics. E.g Height in inches and test scores
etc..
 Qualitative variables: They are sometimes referred to
as categorical variables because they classify by
categories. Ordinal, Nominal variables are qualitative. •
Nominal variables such as gender, religion, or color are
categorical variables.

Continuous and Discontinuous Variables
• Continuous variable: 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.
• Discontinuous variable: 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 discrete variable.

Demographic Variables

Extraneous variable
• Extraneous variable: It happens sometimes that after
completion of the study we wonder that the actual
result is not what we expected. In spite of taking all the
possible measures the outcome is unexpected. It is
because of extraneous variables. Variables that may
affect research outcomes.
• Extraneous variables that are not recognized until the
study is in process, or are recognized before the study is
initiated but cannot be controlled, are referred to as
confounding variables. These variables interferes the
results of the existing activity.

HYPOTHESIS
Anticipated outcome or Possible solutions to the
research questions

Types of Hypothesis (H)
• Simple
• Complex
• Empirical
• Question form
• Null
• Directional
• Non-Directional
• Associative

example: a particular species of flower bloom in a
different color only during a specific season.Deprtment of Education, MSU

It predicts an associative relationship
between the independent and dependent
variable
When there is a change in any one of the
variables, changes also occur in other
variable

What is Population?
Definition:
The group of individuals
The group to which you want to generalize your
findings.
The larger group you are representing with your sample.
Census -- the entire population

What is Sample?
Definition
 A subset of the population
 A portion of the population (e.g., 10% or 25%)
 Sample is the raw material for the researcher.

SAMPLING…
Target Population
Study Population
Sample
When you sample the entire population?
• When your population is very small
• When you have extensive resources

Characteristics of a good sample
• True representative
• Free from bias
• Objective
• Maintained accuracy
• Comprehensive in nature
• Economical - energy, time, and money point of view

1. Define the population
2. Identify the sampling frame
3. Select a sampling design or procedure
4. Determine the sample size
5. Draw the sample
Steps in Sampling Process

Sampling Process

Determining sample size
• Sample size is an important factor in research study.
• How many sample I need to collect? Common question.
‘It depends’ – nature of research, population, research design etc.,
• It is defined by different experts in different ways.
E.g: some are suggested 5% of the population
others stated 10%, 25% etc.,
• Hence there is no hard fast rule.
• It should be neither too small nor too large. It should be Optimum
size.
• If the researcher wants to study intensively of a problem, it is better
to select small sample.

Types of Samples
• Probability (Random) Samples
- Simple random sample
– Systematic random sample
– Stratified random sample
– Cluster sample
• Non-Probability Samples
– Convenience sample
– Purposive sample
– Quota
– Snowball

Simple random sampling:
All members of the population has a chance of
being included in the sample
Ex. Lottery sampling & Throwing dices

 This technique is applied in a heterogeneous population.
Step I : The population is divided into a number of homogeneous
strata or sub-groups based on one or more criteria.
Step II : A sample is drawn from each stratum using simple random
technique.
Types:
– Proportionate Stratified Random Sampling
– Disproportionate Stratified Random Sampling
More representative.
Greater accuracy.
Demerits
Utmost care in dividing strata.

NON PROBABILITY SAMPLING
• Non probability sampling: The member of the population
being chosen in unknown. (these are sometimes referred
to as 'out of coverage'/'undercovered').
• The probability of selection can't be accurately
determined. It involves the selection of elements based on
assumptions. Hence, because the selection of elements is
nonrandom.
(e.g. an unemployed person who spends most of their time at home is more likely to
answer than an employed housemate who might be at work when the interviewer
calls)

Non-Probability Sampling
Definition
The process of selecting a sample from a
population without using (statistical) probability
theory.
Note:
• each element/member of the population DOES NOT have an
equal chance of being included in the sample, and
• the researcher CANNOT estimate the error caused by not
collecting data from all elements/members of the population.

Types of Non-Probability Sampling
1. Convenient Sampling
2. Judgment Sampling
3. Quota Sampling
4. Snowball Sampling

CONVENIENCE SAMPLING
• Also known as opportunity or accidental or haphazard sampling.
• It involves the sample being drawn from that part of the population which is
close to hand. That is, readily available and convenient.
• The researcher using such a sample cannot scientifically make generalizations
about the total population because it would not be representative enough.
• E.g if the researcher wants to conduct a survey at a shopping center early in
the morning on a given day, the people that he/she could interview would be
limited to those given there at that given time, which would not represent the
views of other members of society in such an area, if the survey was to be
conducted at different times of day and several times per week.
• This type of sampling is most
useful for pilot testing.

• In this method the sample being drawn from that part of the
population which is close to hand.
• Convenience sampling is used in research where the
researcher is interested in getting an inexpensive. As the
name implies, the sample is selected because they are
convenient.
• Convenience sampling often leads to a biased study since it
consists of only available people.
• Convenience sampling has little statistical validity.

Advantages
• No need for list of population.
• Collect data quickly and economically.
• Best method for exploratory research.
• It does not require any statistical expertise.
Disadvantages
• It is highly biased, because of the researcher’s subjectivity, and so it does
not yield a representative sample.
• It is the least reliable sampling method.
• The findings cannot be generalized.

Judgment Sampling
• It also called as purposive sampling.
Definition
The researcher select the sample to fulfill a purpose; such as ensuring all
members have a certain characteristic. No randomization.
Example: The researcher want to be sure include members from Tamil
Nadu, Kerala, Karnataka, Andhra in relatively equal numbers.

Advantages
• Moderate cost.
• Generally more appropriate than a convenience sample.
• Sample guaranteed to meet a specific objectives.
• Useful for certain types of forecasting.
Disadvantages
• Requires greater researcher effort.
• Bias due to researcher’s beliefs may make sample unrepresentative.
• Projected data beyond sample inappropriate.

QUOTA SAMPLING
• The population is first segmented into mutually exclusive sub-
groups, just as in stratified sampling.
• Then judgment used to select the units from each segment based
on a specified proportion.
Example: The population is divided into cells on the basis of relevant
control characteristics.
A quota of sample units is established for each cell.
50 women, 50 men
A sample is drawn for each cell until the quota is met.

• For example, On basis of the quota in college year level, the
researcher needs equal representation, with a sample size of
100.
• He must select
25 -1st year students
25 -2nd year students
25 -3rd year and
25 -4th year students.
• The bases of the quota are usually age, gender, education,
race, religion and socioeconomic status.

Advantages
• Moderate cost.
• Very extensively use.
• No need of population list.
Disadvantages
• Bias in researchers classification of units.
• Errors can’t be estimated
• It may not yield a precise representative sample.
• Choose only accessible persons and accessible areas.

Snowball Sampling
• This is the colourful name for the technique of building up a
list or a sample of a special population.
Definition
Selecting participants by finding one or two participants and
then asking them to refer to others.
Selection of additional respondents is based on referrals from
the initial respondents. (Building a sample through referrals)
e.g: friends of friends
• It is usually done when there is a very small population size.

Example 1:
interviewing a homeless person and then asking him to
introduce you to other homeless people you might interview.
Example 2:
if a researcher wants to study the problem faced by Indians
through some source like Indian Embassy. Then he can ask
each one of them to supply names of other Indians known to
them, and continue this procedure until he gets an exhaustive
list.
• In this method the populations are not easily identified or
accessed.

Advantages
• Low cost.
• Used in special situation.
• Useful in locating members of rare populations. .
• It is very useful in studying social groups and informal group in a
formal organization.
• It is useful for smaller populations for which no frames are readily
available.
Disadvantages
• Highly bias because sample units not independent.
• Projecting data beyond sample inappropriate.
• It is difficult to apply this method when the population is large.

Sampling Errors
• The errors which arise because of studying only a part of the
total population are called sampling errors.
• These may arise due to non-representativeness of the
samples and inadequacy of sample size.
• When several samples are drawn from a population, their
results would not be identical. The degree of variations of
sample results is measured by standard deviation (standard
error).
• As sample size increases the magnitude of the error
decreases.
• Sample size and sampling error are thus negatively correlated.

Non-Sampling Error
These are errors which arise from sources other than
sampling. It include errors of
• Observation
• measurement and
• responses etc.,

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