This document discusses sampling methods used in research. It defines key terms like population, sample, element, and describes different sampling techniques. The main sampling methods covered are probability sampling techniques like simple random sampling, systematic sampling and stratified sampling which select samples randomly from the population. It also discusses non-probability sampling techniques like convenience sampling and purposive sampling which do not select samples randomly.

1. Sampling

2. • Sampling
• Population
• Elements
• Subject
• Sampling unit
• Sampling process
• Sampling units

3. Sampling
• The process of selecting right individual objects,
or events as representative for the entire
population is known as sampling.
For example
• Sensex-30 companies
• Nifty- 50 companies

4. Population
• The population refers to the entire group of
people, events or things of interest that
researcher wishes to investigate and wants to
make inferences.
For example
• If researcher wants to know the investment
pattern of Mumbai city then all residents of
Mumbai will be the population.

5. Element
• An element is a single member of the
population.
e.g.
• Total students of GICED is 1800, then each
student will be the element.

6. Sample
• It is a subject of the population
• It comprises some members selected from it
• In other words, some, but not all elements of the
population form sample.
• By studying the sample, the researcher should be
able to draw conclusion that are generalized to the
population of interest
E.g.
• Sensex-BSE
• Nifty-NSE

7. Sampling unit
• It is the element or set of element that is
available for set selection in some stage of the
sampling process

8. Subject
• It is a single member of the sample just as an
element of the population.

9. Reasons for sampling
• Practically impossible to collect data from, or
examine every element
• Even it is possible, it is prohibitive in terms of
time, cost and human resources
• Study of sample rather than population is also
sometimes likely to produce more reliable data
especially when a large number of elements is
involved
• E.g. election survey by media

10. Representative
Sample -
• σ 2
• σ
• Population –
µ
• σ 2
• σ

11. Sampling process
• Define population
• Determine sample frame
• Determine sample design
• Determine appropriate sample size
• Execute sampling process

12. Define population
• It must be defined in terms of elements,
geographical boundaries and time

13. Determine sample frame
• It is a representation of all the elements in the
population from which the sample is drawn
E.g.
• The payroll of an organization would serve as the
sampling frame if its members are to be studied

14. Sampling design
Two major types of sampling design
• Probability
• Non probability

15. Probability
• The elements in the population have some
known, non-zero chance or probability of being
selected as a sample object
• This design is used when the representative of
the sample is of importance in the interest of
wider generalizability

16. • Non probability
• Elements in the population do not have a known
or predetermined chance of being selected as a
subject
• When time and other factors, rather than
generalizability, become critical, non probability
sampling is generally used

17. Determine sample size
• Is a large sample better than a small sample?
• Decision about how large the sample size should
be a very difficult one.
• We can summarize the factors affecting factors
affecting decision on sample size as
• Research objective
• Cost and time constraint
• Amount of the variability in the population itself

18. Probability sampling
1. Unrestricted or simple random sampling
2. Restricted or complex probability sampling
• Systematic sampling
• Stratified random sampling
• Cluster sampling
• Double sampling or Area sampling

19. Unrestricted or simple random sampling
• In this sampling every element in the population
has known and equal chance of being selected as
a subject
• This sampling design, has the least bias and
offers the most generalizability
• However this sampling process could become
cumbersome and expenive

20. Restricted or complex probability sampling
• These probability sampling procedures offer a
viable and sometimes more efficient design
• More information can be obtained

21. Systematic sampling
It involves drawing every nth element in the
population starting with randomly chosen
element between 1 & nth element

22. Stratified random sampling
• It involves a process of segregation followed by
random selection of subject from each stratum

23. Cluster sampling
• In this target population is first divided into
clusters
• A specific cluster sampling is area sampling

24. • Double sampling
• When further information is needed from subset
of the first group from which some information
has already been collected for the same study.

25. Non probability sampling
• There are two types
1. Convenience
2. Purposive

26. Convenience
• Collection of information from members of the
population who are conveniently available to
provide it.

27. Purposive
1. Judgment
2. Quota

28. 1. Judgment
• Most advantageously placed
• In the best position to provide information
required

29. Quota
• Certain group