Presentation on Sampling in Research.

1. SAMPLING

3. • Population or Universe: It refers to the group of people, items or
units under investigation and includes every individual.
• Sample: a collection consisting of a part or subset of the objects or
individuals of population which is selected for the purpose,
representing the population
• Sampling: It is the process of selecting a sample from the
population. For this population is divided into a number of parts
called Sampling Units.

4. Census vs. Sampling
• Two methods of selecting the respondents
– Census
– Sampling
• Census
– When the number of respondents / units of interest are limited, or
– When it is required to gather data from all the individuals in the
population

5. • Sampling
– When the size of the population is too large
– The population is homogeneous
– Considerations of time and cost play a major role in going for
sampling.

6. NEED OF SAMPLING
• Large population can be conveniently covered.
• Time, money and energy is saved.
• Helpful when units of area are homogenous.
• Used when percent accuracy is not acquired.
• Used when the data is unlimited.

7. ADVANTAGES OF SAMPLING
• Economical: Reduce the cost compare to entire population.
• Increased speed: Collection of data, analysis and Interpretation of
data etc. take less time than the population.
• Accuracy: Due to limited area of coverage, completeness and
accuracy is possible.
• Rapport: Better rapport is established with the respondents, which
helps in validity and reliability of the results

8. DISADVANTAGES OF SAMPLING
Biasedness: Chances of biased selection leading to incorrect conclusion.
Selection of true representative sample: Sometimes it is difficult to
select the right representative sample.
Need for specialized knowledge: The researcher needs knowledge,
training and experience in sampling technique, statistical analysis and
calculation of probable error.
Impossibility of sampling: Sometimes population is too small or too
heterogeneous to select a representative sample.

9. Sampling Process
Specification of objectives: The objectives of carrying out
sampling is the first and foremost step in sampling
procedure. Because all other steps will follow from this
objective.

10. Definition of population:
In this step we define the units that should be included in the
population.
For example, if you want to survey the library personnel, it has to be
decided whether you should include part-time employees in the
population or not.

11. Identify the sampling frame:
– Need to clearly define from which universe will the
sample be picked from.

12. Identification of sampling procedure:
Sampling procedure refers to the method of selecting the sample. There are quite a few
sampling procedures available.
We should select a method that:
i) gives us a representative sample
ii) is feasible to carry out keeping in view our constraints
iii) is cost effective.
There are broadly two types of sampling: Probability sampling and Non-Probability
sampling.

13. Determination of sample size:
The next step is the determination of sample size. The factors that influence sample size
are
i) population size
ii) variance of units
iii) desired precision level
iv) response rate
v) availability of resources.
The sample size should be relatively larger if population size is larger, variability among
units is higher, more precise results are required, and lesser is the response rate.

14. Specify the sampling unit :
– We need to decide on whom to contact in order to obtain the data
required
– Need to be careful while selecting the sampling unit, as we need to
be sure of whether we will get the required data from the respondent
or not.

15. TYPES OF SAMPLING
1. Probability Sampling: A probability sample is one in which each
member of the population has an equal chance of being selected.
2. Non-Probability Sampling: Nonprobability Sample a particular
member of the population being chosen is unknown.
• In probability sampling, randomness is the element of control. In
Non-probability sampling, it relies on personal judgment.

16. • Population divided
into clusters, random
sample of clusters is
selected from as
simple random design
• Population is
divided into sub-
population/stratum
& subject selected
randomly.
• Every nth element
chosen started at
random & picking
every nth element in
succession.
• All elements are
considered and each
has equal chance of
being selected
SIMPLE
RANDOM
SYSTEMATIC
CLUSTERSTRATIFIED

17. Simple Random Sampling
Here all members have the same chance (probability) of being
selected. Random method provides an unbiased cross selection of the
population.
For Example,
We wish to draw a sample of 50 students from a population of 400
students. Place all 400 names in a container and draw out 50 names
one by one.

18. Systematic Sampling
Each member of the sample comes after an equal interval from its
previous member.
For Example,
for a sample of 50 students, the sampling fraction is 50/400 = 1/8 i.e.
select one student out of every eight students in the population. The
starting points for the selection is chosen at random.

19. Stratified Sampling
The population is divided into smaller homogenous group or strata by some
characteristic and from each of these strata members are selected randomly.
Finally from each stratum using simple random or systematic sample method is
used to select final sample.

20. Cluster Sampling (Area Sampling)
A researcher/enumerator selects sampling units at random and then
does complete observation of all units in the group.
For example, the study involves Primary schools. Select randomly 15
schools. Then study all the children of 15 schools. In cluster sampling
the unit of sampling consists of multiple cases. It is also known as
area sampling, as the selection of individual member is made on the
basis of place residence or employment.

21. Non-Probability Sampling
• Referred
by current
sample
elements
• Relevant
characteristics are
used to segregate
the sample to
improve its
representatives
• Deliberately
select sample
to conform to
some criteria
• Based on
ease
accessibility
CONVINEINCE JUDGEMENTAL
SNOWBALLQUOTA

22. Purposive Sampling
In this sampling method, the researcher selects a "typical
group" of individuals who might represent the larger
population and then collects data from this group. Also
known as Judgmental Sampling.

23. Convenience Sampling
It refers to the procedures of obtaining units or members who
are most conveniently available. It consists of units which
are obtained because cases are readily available.
In selecting the incidental sample, the researcher determines
the required sample size and then simply collects data on that
number of individuals who are available easily.

24. Quota Sampling
The selection of the sample is made by the researcher, who decides
the quotas for selecting sample from specified sub groups of the
population.
• For example, an interviewer might be need data from 40 adults and
20 adolescents in order to study students’ television viewing habits.
Selection will be
• 20 Adult men and 20 adult women
• 10 adolescent girls and 10 adolescent boys

25. Snowball Sampling
In snowball sampling, the researcher Identifying and selecting
available respondents who meet the criteria for inclusion.
After the data have been collected from the subject, the researcher
asks for a referral of other individuals,
who would also meet the criteria and
represent the population of concern.

26. Probability Sampling vs Non-Probability Sampling
Probability Sampling Non-Probability Sampling
You can generalize to the population designed
by the sampling frame
You cannot generalize beyond the sample
Allows use of statistics, tests hypotheses Exploratory research, Generates hypotheses
Can estimates population parameters Population parameters are not of interest
Eliminates biases Adequacy of the sample can’t be known
Must have random selection of units Cheaper, easier, quicker to carry out

27. REFERENCE
• MLIE-102 Research Methodology (2017) Indira Gandhi
National Open University(IGNOU).