Sampling is procedure or process of selecting some units from the population with some common characteristics and is primarily concerned with the collection of data of some selected units of the population.

1. Sampling

2. introduction
• Most survey work involves sampling from finite
populations.
• There are two parts to any sampling strategy
(design).
– First, there is a selection procedure, the manner in
which sampling units are selected from a population.
– Second, there is an estimation procedure that
prescribes how inferences are to be drawn from
sample to the population

3. • Sampling is procedure or process of selecting
some units from the population with some
common characteristics and is primarily
concerned with the collection of data of some
selected units of the population.
• Census is another method of data collection and
is defined as a complete enumeration of the
population.

4. Definition of sampling terms
Sample It is a part of population, which is selected at random
Sampling
• Sampling is a process of selecting a sample from the population
Sampling unit (element)
• Any basic item which is selected for the purpose of sampling
– Example: children <5 years etc

5. Definition of sampling terms
Sampling Frame
• A complete list of population from which a sample is to be
selected
– Example: Voters list, Name of students in a
university
Sampling fraction
• Ratio between sample size and population size
– Example: 100 out of 2000 (5%)

6. Sampling Error
• Since sample is a part of population, the result based on
the sampled observations will not be equal to that of
population values. There must be some difference, which is
inevitable. This difference is known as error.
• This error is arising due to drawing inferences about the
population on the basis of sampled observations, therefore,
it is termed as sampling error.
• For Instance the prevalence of tuberculosis based on a
sample cannot be identical to its prevalence in the
population.

7. Note
• The sampling error usually decreases as the
sample size increases.

8. Non sampling error
• Error arising from the causes not associated with the sampling process is
known as non- sampling error.
• It is common, both to complete enumeration and sample surveys and
Includes
– (i) response error
– (ii) non-response error
– (iii) measurement and coding error
– (iv) improper method for statistical analysis
– (v) non- coverage of population
– (vi) interviewers error
– vii) data entry error etc.
• As the sample size increases, non-sampling error increases.

9. Advantages to select a sample from a population
It Includes
• A sample is a part of population; the information can be collected
more cheaply and more rapidly as compared to complete
enumeration.
• A sample makes it possible to concentrate on individual units and to
obtain relevant information comprehensively and accurately.
• Selection of appropriate sampling design reduces non-sampling
error.
• More precise results can be obtained by survey and sampling
experts.

11. Types of Sampling
• There are, generally, two types of sampling,
i.e.
– (i) probability sampling.
– (ii) non- probability sampling.

12. Probability Sampling
• A probability sample or a random sample is one
in which the probability of selection of each unit
in the population is known.
• The probability of selection of each unit may or
may not be independent.
• If a sample is selected at random then it is known
as a probability sample.

13. Probability sampling
• Simple random sampling
• Stratified sampling
• Systematic sampling
• Cluster sampling

14. Simple random Sampling
• In simple random sampling each and every unit of the
population has an equal probability of its being included in the
sample.
• It is applied to the population when it containing homogenous
material.
• Random sample can be drawn by
a) Lottery system
b) Random marking method

15. Systematic Random Sampling
• This is the form of the random sampling, involving a
system. The system is one of regularity. The sampling
frame is chosen and a name or unit is chosen at random.
Then from this chosen name or unit every nth item is
selected throughout the list.

16. Example: Systematic sampling
• N = 1200, and n = 60
 sampling fraction = 1200/60 = 20
• List persons from 1 to 1200
• Randomly select a number between 1 and 20 (ex : 8)
 1st person selected = the 8th on the list
 2nd person = 8 + 20 = the 28th etc .....

17. Systematic Sampling
Select some starting point and then
select every K th element in the population

18. Stratified random Sampling
• This is form of random sampling in which all peoples or
items in the sampling frame are divided into groups or
categories which are mutually exclusive (that is, a person
or unit can be in one group only) these groups are called
‘strata’.
• With in each of these group (stratum) a simple random
sample is selected.

19. Cluster Sampling
• In many situations, the sampling frame for elementary
units of the population is not available, moreover it is not
easy to prepare it. But the information is available for
groups of elements so called clusters.
• For instance, the list of houses may available but not the
persons residing in them. In this situation houses are
known as clusters and selection has to be made of houses
in the sample.
• Such a sampling procedure is known as cluster sampling.

20. Cluster Sampling
divide the population into sections
(or clusters); randomly select some of those clusters;
choose all members from selected clusters

23. Non-Probability Sampling
• A sample selected by a non-random process is termed as a non-
probability sample.
• Judgment samples (purposive samples) and quota samples are
examples of non-probability samples.
• These types of selection procedures are useful when the population
units are highly variable and the sample is small.
• In these selection procedures, there is no way to check the
precision and to obtain the precise estimates.
• There is no way to determine the sampling, non-sampling errors.

24. Convenience sample
• A convenience sample simply includes the individuals who happen
to be most accessible to the researcher.
• This is an easy and inexpensive way to gather initial data, but there
is no way to tell if the sample is representative of the population, so
it can’t produce generalizable results.
Example
– You are researching opinions about student support services in your
university, so after each of your classes, you ask your fellow students
to complete a survey on the topic. This is a convenient way to gather
data, but as you only surveyed students taking the same classes as you
at the same level, the sample is not representative of all the students
at your university.

26. Quota Sampling
• A type of non-probability sample in which the
researcher establishes quotas for recruitment
of subjects into the sample so that the sample
will similar to the population on selected
characteristics.

29. Snow Ball Sampling
• If the population is hard to access, snowball sampling
can be used to recruit participants via other
participants. The number of people you have access to
“snowballs” as you get in contact with more people.
• Example
– You are researching experiences of homelessness in your
city. Since there is no list of all homeless people in the city,
probability sampling isn’t possible.

31. Purposive sampling
• This type of sampling, also known as judgment sampling,
involves the researcher using their expertise to select a
sample that is most useful to the purposes of the research.
• It is often used in qualitative research, where the
researcher wants to gain detailed knowledge about a
specific phenomenon rather than make statistical
inferences, or where the population is very small and
specific. An effective purposive sample must have clear
criteria and rationale for inclusion.

32. Purposive sampling
Example
• You want to know more about the opinions
and experiences of disabled students at your
university, so you purposefully select a
number of students with different support
needs in order to gather a varied range of data
on their experiences with student services.

34. • Thank You