Department of Business Administration
Sadakathullah Appa College ( Autonomous)
• 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
• Continuous research in any field provides
fruitful aspects for the future.
• Government and NGO sector
• Business sector
• Social sector
• 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
• 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.
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
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
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.
• 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.
Anticipated outcome or Possible solutions to the
It predicts an associative relationship
between the independent and dependent
When there is a change in any one of the
variables, changes also occur in other
What is Population?
The group of individuals
The group to which you want to generalize your
The larger group you are representing with your sample.
Census -- the entire population
What is Sample?
A subset of the population
A portion of the population (e.g., 10% or 25%)
Sample is the raw material for the researcher.
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
• 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
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
• 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
Simple random sampling:
All members of the population has a chance of
being included in the sample
Ex. Lottery sampling & Throwing dices
Stratified random sampling
• Entire population divided into a number of
homogeneous groups or types or class called strata
Example-Teachers in Tirunelveli District
Population – Homogeneous or Heterogeneous
In case of a homogeneous population, even a
simple random sampling will give a
If the population is heterogeneous, stratified
random sampling is appropriate.
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
(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
The process of selecting a sample from a
population without using (statistical) probability
• 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.
• 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
• Convenience sampling often leads to a biased study since it
consists of only available people.
• Convenience sampling has little statistical validity.
• No need for list of population.
• Collect data quickly and economically.
• Best method for exploratory research.
• It does not require any statistical expertise.
• 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.
• It also called as purposive sampling.
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.
• Moderate cost.
• Generally more appropriate than a convenience sample.
• Sample guaranteed to meet a specific objectives.
• Useful for certain types of forecasting.
• Requires greater researcher effort.
• Bias due to researcher’s beliefs may make sample unrepresentative.
• Projected data beyond sample inappropriate.
• The population is first segmented into mutually exclusive sub-groups, just as in
• Then judgment used to select the units from each segment based on a specified
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 interviewers might be tempted to interview those who look most
helpful. The problem is that these samples may be biased because not everyone gets
a chance of selection.
• For example, On basis of the quota in college year level, the
researcher needs equal representation, with a sample size of
• 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.
• Moderate cost.
• Very extensively use.
• No need of population list.
• 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.
• This is the colourful name for the technique of building up a
list or a sample of a special population.
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.
interviewing a homeless person and then asking him to
introduce you to other homeless people you might interview.
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
• In this method the populations are not easily identified or
• 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
• It is useful for smaller populations for which no frames are readily
• Highly bias because sample units not independent.
• Projecting data beyond sample inappropriate.
• It is difficult to apply this method when the population is large.
• 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
• As sample size increases the magnitude of the error
• Sample size and sampling error are thus negatively correlated.
These are errors which arise from sources other than
sampling. It include errors of
• measurement and
• responses etc.,