This document discusses different sampling techniques used in research. It describes probability sampling methods like simple random sampling, stratified sampling, cluster sampling, systematic sampling, and multistage sampling. It also covers non-probability sampling techniques including convenience sampling, judgement sampling, quota sampling, and snowball sampling. For each method, it provides advantages and disadvantages in 1-2 sentences. The document aims to help understand key sampling designs and their appropriate uses in research studies.

1. GIDC RAJJU SHROFF ROFEL
INSTITUTE OF MANAGEMENT STUDIES
Submitted To: Prof. Zankhana Atodaria
GROUP
MEMBERS
ENROLLMENT
NO.
KAJAL TIWARI 2067
JUHI SINGH 2065
URVASHI TIWARI 2068
JAINAB KHALIFA 2021
SAURABH JHA 2024
FARAH SHAIKH 2060
ANJALI SINGH 2064

2. SAMPLE
It is a unit that is selected from population.
Represent the whole population.
Purpose to draw the interface.

3. SAMPLING
Sampling is the process of selecting observations
(a sample) to provide an adequate description
and inferences of the population.

4. SAMPLING DESIGN

5. SAMPLING DESIGN PROCESS
DEFINE POPULATION
DETERMINE SAMPLING FRAME
DETERMINE SAMPLING PROCEDURE
 PROBABILITY SAMPLING:
1) SIMPLE RANDOM SAMPLING
2) STRATIFIED SAMPLING
3) CLUSTER SAMPLING
4) SYSTEMATIC SAMPLING
5) MULTISTAGE SAMPLING
 NON-PROBABILITY SAMPLING:
1) CONVENIENT SAMPLING
2) JUDGEMENTAL SAMPLING
3) QUOTA SAMPLING
4) SNOWBALL SAMPLIMG
DETERMINE APPROPRIATE SAMPLE
SIZE
EXECUTE SAMPLING DESIGN

6. SAMPLING TECHNIQUES
There are lot of sampling techniques which
are grouped into two categories as,
1) Probability Sampling
2) Non- Probability Sampling
The difference lies between the above two is
whether the sample selection is based on
randomization or not. With randomization,
every element gets equal chance to be
picked up and to be part of sample for
study.

7. PROBABILITY SAMPLING
Probability sampling is based on the fact that
every member of a population has a known
and equal chance of being selected.
 For example, if you had a population of 100
people, each person would have odds of 1 out
of 100 of being chosen.

8. SIMPLE RANDOM SAMPLING
All subsets of the frame are given an equal
probability.
Random numbers are generators.

9. SIMPLE RANDOM SAMPLING
ADVANTAGES:
1) Equal and independent
chances of selection to every
element.
2) No need of prior information
of population.
3) Sampling error easily
measured.
4) Easy to analyse data.
DISADVANTAGES:
1) Low frequency of use.
2) Does not use researchers’
expertise.
3) Larger risk of random
error.

10. STRATIFIED RANDOM SAMPLING
Population is divided into two or more groups
called strata.
Subsamples are randomly selected from each
strata.

11. STRATIFIED RANDOM SAMPLING
ADVANTAGES:
1) Assures representation of
all groups in sample
population.
2) Characteristics of each
stratum can be estimated
and comparisons.
DISADVANTAGES:
1) Required accurate
information on proportion
of each stratum.
2) Stratified list costly to
prepare.

12. CLUSTER SAMPLING
The population is divided into subgroups
(clusters) like families.
A simple random sample is taken from each
cluster.

13. CLUSTER SAMPLING
ADVANTAGES:
1) Can estimate characteristics
of both cluster and
population.
DISADVANTAGES:
1) The cost to reach an
element to sample is very
high.
2) Each stage in cluster
sampling introduces
sampling error – the
more stages there are, the
more error there tends to
be.

14. SYSTEMATIC RANDOM SAMPLING
Order all unit in the sampling frame.
Then every nth number on the list is selected.
N = Sampling Interval

15. SYSTEMATIC RANDOM SAMPLING
ADVANTAGES:
1) Moderate cost; moderate
usage.
2) Simple to draw sample.
3) Easy to verify.
DISADVANTAGES:
1) Periodic ordering required.

16. MULTISTAGE SAMPLING
Carried out in
stages.
Using smaller
and smaller
sampling units
at each stage.

17. MULTISTAGE SAMPLING
ADVANTAGES:
1) More accurate.
2) More effective.
DISADVANTAGES:
1) Costly.
2) Each stage in sampling
introduces sampling error
– the more stages there
are, the more error there
tends to be.

18. NONPROBABILITY SAMPLING
This type of sampling is also known as non-
random sampling.
The probability of each case being selected
from the total population is not known.
Units of the sample are chosen on the basis
of personal judgment or convenience.

19. CONVENIENCE SAMPLING
Convenience sampling involves choosing
respondents at the convenience of the
researcher.
This method is used when the availability of
sample is rare and also costly. So based on
the convenience samples are selected.

20. CONVENIENCE SAMPLING
ADVANTAGES:
1) Very low cost.
2) Extensive used/understood.
DISADVANTAGES:
1) Variability and bias can not
be measured or controlled.
2) Projecting data beyond
sample not justified.
3) Restriction of generalization.

21. QUOTA SAMPLING
The population is first segmented into
mutually exclusive sub-groups, just an in
stratified or cluster sampling.
For example: If our population has 45%
females and 55% males then our sample
should reflect the same percentage of males
and females.

22. QUOTA SAMPLING
ADVANTAGES:
1) Used when research
budget is limited .
2) Very extensive used/
understood.
3) No need for list of
population elements.
DISADVANTAGES:
1) Variability and bias
cannot be measured /
controlled.
2) Time consuming.
3) Projecting data beyond
sample not justified.

23. JUDGEMENTAL SAMPLING
Judgemental Sampling is also known as
Purposive Sampling. This is based on the
intention or the purpose of study.
For Example: If we want to understand the
thought process of the people who are
interested in pursuing master’s degree then the
selection criteria would be “Are you interested
for Masters in..?”
 All the people who respond with a “No” will be
excluded from our sample.

24. JUDGEMENTAL SAMPLING
ADVANTAGES:
1) There is a assurance of
quality response.
2) Meet the specific objective.
DISADVANTAGES:
1) Bias selection of sample
may occur.
2) Time consuming process.

25. SNOWBALL SAMPLING
The research start with a key person and
introduce the next one to become a chain.

26. SNOWBALL SAMPLING
ADVANTAGES:
1) Low cost.
2) Useful in specific
circumstances & for
locating rare population.
DISADVANTAGES:
1) Not independent.
2) Projecting data beyond
sample not justified.

27. SAMPLING ERRORS
The errors which arise due to the use of
sampling surveys are known as the sampling
errors.
Two types of sampling errors:-
1) Biased error- Due to selection of sampling
techniques; size of the sample.
2) Unbiased error/Random sampling errors-
Differences between the members of the
population included or not included.