2.
Introduction
When data is to be collected from each
member of the population, it is known as
Census Survey
When data is to be collected only from some
members of the population, it is known as
Sample Survey
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3.
Population
Population is defined as The Entire Group under
study. Sometimes it is also called as the
“Universe.”
4.
Sampling Design
Sampling is defined as the practice of taking a
small part of a large bulk to represent the whole.
Its main objective is to secure a sample which,
subject to limitations of size will reproduce the
characteristics of the entire population as closely
as possible.
Sampling procedures are therefore compared to
a mirror which gives a reflection true to the
original.
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5.
Definition of Sampling
According to Non Lin, “sampling
design is a subset of cases from the
population chosen to represent it. By
using the subset, we can infer the
characteristics of the population.”
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6.
Census Vs Sampling
Size of population
Amount of Funds for the study
Facilities
Time
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7.
Characteristics of a Good Sample
Representativeness
Accurate (Unbiased)
Precision
Adequate in Size
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8.
Advantages of Sampling
Reduces time and cost
Saves labour
Quality of Study is Better
Provides quicker results
Effective if population is infinite
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9.
Limitations of Sampling
Results obtained may be incorrect or
misleading
A large sample has all drawbacks of Census
Survey
Complicated sampling may require more
labour
Representativeness may not be possible in
certain cases
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10.
METHODS OF SAMPLING
The methods of sampling can be divided on the
basis of the element of probability associated
with the sampling technique. Probability means
chances available to members of the population
for getting selected in the sample. Accordingly,
the methods of sampling are classified into two
broad types:
Probability Sampling
Non Probability Sampling
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11.
METHODS OF SAMPLING
PROBABILITY METHOD
NON PROBABILITY METHOD
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Area Sampling
Multistage and Multiphase Sampling
Accidental Sampling
Convenience Sampling
Judgment Sampling
Purposive Sampling
Quota Sampling
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12.
Probability Sampling Method
Probability Sampling is also known as Random Sampling
Probability means chance
Therefore element of the population has known chance
or opportunity of being selected in the sample
Eg. If a sample of 100 students is to be selected from a
population of 1000 students, then it is known to every one
that each student has 1000 / 100 i.e. 1 chance in 10 being
selected
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13.
Features of Probability Sampling
It is the only systematic and objective method of
sampling that provides equal chance to every element
of the population in getting selected in the sample
The results of probability sampling more accurate
and reliable
It helps in the formulation of a true representative
sample by eliminating human biases
Under probability method each element of
population known in advance about the possibility of
being included in the sample
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14.
Types of Probability Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Area Sampling
Multistage and Multi Phase Sampling
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15.
Simple Random Sampling
It is the basic probability sampling technique and
all other methods are variations of simple random
method.
It can be defined as the method of sampling which
provides every element in the population an equal
and known chance of being selected in the sample.
Simple random can be done by
A) Lottery Method
B) Random Tables
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16.
Lottery Method
In this method every unit of the population is
identified with a number or slip
This small number chits are placed in a box,
well mixed and then a person is asked to take
out a lucky slip
This process is continued until the required
size of the sample achieved
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17.
Random Method
It provides use of random numbers specially
designed for sampling purposes
Such type of random table are mostly found
at the end of statistical textbooks
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18.
Systematic Sampling
It is modification of simple random sampling. It
is called as quasi-random sampling.
It is called quasi because it is in between
probability and non-probability sampling.
The procedure of quasi sampling begins with
finding out the sample interval. This can be
found out by the ratio of the population to the
sample. Afterwards a random number is
selected from the sample interval.
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19.
Illustration of Systematic Sampling
Selecting a sample of 100 students out of 1000, the sample
interval will be 1000 divided by 100 i.e.10.
Then make small chits bearing numbers 1to 10 and put them into
a box
Then by using lottery method withdraw one slip and suppose we
get number 5 then proceed to select numbers starting with 5 with
a regular interval of 10.
The selected sample consists of elements bearing nos.
5,15,25,..........105,115 and so on .
It should be noted that up to selecting no.5,Systematic sampling
can be treated as probability sampling and afterwards it is nonprobability because the chances of other elements are certainly
affected
In this example numbers other than 5 have no chance of being
selected
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20.
Stratified Random Sampling
In this method, the population is divided and subdivided with
homogeneous or similar characteristics
For example, a group of 200 college teachers can be first
divided into teachers in Arts faculty, Commerce Faculty and
Science Faculty.
After dividing the entire population of teachers into such
classes called strata, a sample is selected from each stratum of
teachers at random. These samples are put together to form a
single sample.
Stratified random sampling is more accurate and
representative as compared to simple random sampling
because under this the population is divided into
homogeneous groups.
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21.
Cluster Sampling
Cluster means group, therefore, sampling units are
selected in groups.
Cluster sampling is an improvement over stratified
sampling. Both simple random and stratified
random sampling are not suitable while dealing
with large and geographically scattered
populations. Therefore, large-scale sample surveys
are conducted on cluster sampling basis.
The working of cluster sampling is based on the
principle that it is beneficial to use a large sample
of units closer to each other than to select a small
group of sample scattered over a wider area.
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22.
Illustration of Cluster Sampling
Suppose researcher wants to study the
learning habits of the college students from
Mumbai. He may select the sample as under
1)First prepare a list of all colleges in Mumbai
city
2)Then, select a sample of colleges on random
basis. Suppose there are 200 colleges in
Mumbai, then he may select 20 colleges by
random method.
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23.
3)From the 20 sampled colleges, prepare a list
of all students. From these lists select the
required number of say 1000 students on
random basis]
In this example the researcher gets a sample
1000 students from 20 colleges only otherwise if
researcher decides to select 1000 students on
random basis, then he would have to select
them out of 200 colleges which would have
been expensive and time consuming
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24.
Area Sampling
It is a method of cluster sampling and used in connection
with selection of samples area with help of maps.
The procedure is to divide the large areas into several
small areas.
For example, The city of Mumbai can be divided on the
basis of municipal wards of zone
A random selection of this is made within each of the
areas selected; a sub sample of locality or sample of
residence is taken and then investigated.
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25.
Multistage and Multi Phase Sampling
As the name suggests, multistage sampling is
carried out in steps. This method is regularly
used in conducting national surveys on large
scale. It is an economical and time saving
method of selecting a sample out of widely
spread population.
In this method first the population will be
divided on state basis, then districts, then
cities, then locality, wards, individuals who
are sampled at different stages until a final
sample unit.
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26.
Multiphase sampling is slightly different from
multi-stage sampling. With multi-phase
sampling, the sampling unit at each phase is
the same, but some of them are interviewed in
detail or asked more questions than others ask.
In other words, all the members of the sample
provide basic information and some of them
provide more and detailed information.
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27.
Non-Probability Sampling
Non-probability sampling is also called as
judgment sampling.
In case of non-probability sampling, units in the
population do not have an equal chance or
opportunity of being selected in the sample. The
non-probability method believes in selecting the
sample by choice and not by chance.
Non-probability sampling suffers defects like
personal bias and sampling error cannot be
estimated.
This is an unscientific and less accurate method of
sampling, hence it is only occasionally used in
research activities.
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29.
Accidental Sampling
Under this method, researcher does not take special
efforts to select the sample, but simply selects those
who are immediately available.
Suppose, the researcher wants to survey 200
people, and then he may consider the first 200
persons he comes across for collecting information.
This method is less expensive, time saving and
generally used by journalists to know public views
on a particular issue. The method useful where too
much accuracy is not required.
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30.
Convenience Sampling
In convenience sampling, the sample is selected as per the
convenience of the researcher.
For example, the producer may add a reply coupon along
with product to collect responses from consumers. The
duly returned coupons are conveniently available to the
researcher for the survey purpose.
Manufacturers of consumer goods like Titan watches and
Philips provide a questionnaire along with the product
purchased and collect information relating to name of
retail store, income group etc., similarly sample selected
from the telephone directory, pay-roll register, register of
members is a type of convenience sampling.
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31.
Judgment Sampling
The sample selected on someone’s knowledge, experience
and judgment is called judgment sampling.
For selecting a sample of residents from a locality, the
researcher may ask and take help from the senior
investigators or those who are well acquainted with the
locality.
The researcher relies on the experience of seniors
because they have better knowledge and idea about the
locality than the researcher.
This method is certainly better than arbitrary sampling as
it makes sampling more representatives.
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32.
Purposive Sampling
Purposive sampling means deliberate selection of
sample units confirm to some predetermined
criteria. This is also known as judgment sampling
It involves selection of cases when we judge as most
appropriate ones for a given study. It is based on
the judgment of a researcher. It does not aim at
securing a cross section of a population. The
selection of samples depends upon the subjective
judgment of researcher.
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33.
Quota Sampling
Quota sampling is the frequently used method of
sampling in marketing research. The basic objective of
quota sampling is to control biases arising out of nonprobability method by stratification and the setting of
quotas for each stratum.
For instance, a sample of 40 students can be selected
from a group of 200 students comprising of 120 boys
and 80 girls. To make the sample representative, the
group of 40 should include 24 boys and 16 girls (i.e.
120: 80 = 3: 2).
Quota sampling offer benefits of speed, economy and
simplicity. It is widely used in market surveys and
public opinion polls.
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34.
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