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