1. Types of Sampling
SUBMITTED TO:
DR. ABHEY SINGH GODARA
SUBMITTED BY:
TANYA
DEPARTMENT OF ECONOMICS
CDLU, SIRSA
2. Sample
A Sample is basically a subset of a population as a whole.
In research, the population does not mean only human population all the
times, it can be factories, Schools, etc.
3. Sampling
The act, process, or technique of selecting a representative part of a
population for the purpose of determining parameters or
characteristics of the whole population.
5. Probability Sampling
It refers to the sampling method in which all the members of the
population has an equal chance to be a part of the sample.
This technique is based on the randomization principle.
This helps to reduce the possibility of bias.
6. Non Probability Sampling
All the individuals of the universe are not given an equal opportunity
of becoming a part of the sample, the method is said to be Non-
probability sampling.
there is no probability attached to the unit of the population and the
selection relies on the subjective judgment of the researcher.
the conclusions drawn by the sampler cannot be inferred from the
sample to the whole population.
7. Types of Probability Sampling
Simple Random Sampling Method :
Random sampling means the arranging of conditions in such a manner that every item of the
whole universe from which we are to select the sample shall have the same chance of being
selected as any other item.the selection of items entirely depends on luck or probability; therefore, this sampling technique is also sometimes known
as a method of chance
Steps of Simple Random Sampling:
Involves listing or cataloguing of all the elements in the population and assigning them
consecutive numbers.
Deciding upon the desired sample size.
A certain number of elements from the list is selected.
8. • Most basic, simple and easy method
• Provides a representative sample
• It is free from subjectivity and free from personal
error.
Advantages of
Sampling Techniques
• In most cases it is difficult to find data list of
all units of the population to be sampled.
• The task of numbering every unit before the
sample is chosen is time consuming and
expensive.
• The units need not only to be numbered but
also arranged in a specified order.
• The possibility of obtaining a poor or
misleading sample is always present when
random selection is used.
Disadvantages of
Sampling Techniques
9. Methods of Drawing, Sample in Random Method
Lottery Method
Tippets Numbers
Tossing a Coin
Throwing a Dice
Pack of Cards
10. Systematic Sampling Method
Systematic sampling is a probability sampling method in which a random
sample, with a fixed periodic interval, is selected from a larger population.
This interval, called the sampling interval, is calculated by dividing the
population size by the desired sample size.
Researchers calculate the sampling interval by dividing the entire population
size by the desired sample size.
11. Advantages:
It is frequently used because it is simple, direct and in- expensive.
When a list of names or items is available, systematic sampling is often an efficient
approach.
Disadvantages:
One should not use systematic sampling in case of exploring unfamiliar areas because
listing of elements is not possible
When there is a periodic fluctuation in the characteristic under examination in relation
to the order in which the items appear, the methods is ineffective
12. Stratified Random Sampling Method
When the population is divided into different Homogenous strata or groups
and then samples are selected from each stratum by simple random sampling
procedure or by regular interval method, we call it as stratified random
sampling method.
❖ Educated women
❖ Un-educated women
❖ Educated men
❖ Un educated men
13. Advantages:
1. Each group have a surety of being represented in the sample. In case of random sample,
there is possibility that bigger groups have greater representation and the smaller groups
are often eliminated or under represented.
2. Stratified random sampling works well for populations with a variety of attributes.
3. Stratification gives a smaller error in estimation.
Disadvantages:
1. It is difficult for the researcher to decide the relevant criterion for stratification.
2. It is costly and time consuming.
3. Unless there are extreme differences between the strata, the expected proportional
representation would be small.
4. One must know the characteristics of the specified population in which the study is to be
made.
14. Types of Stratified
Random Sampling
Disproportionate
stratified sampling
Proportionate
stratified sampling
In Proportionate Stratified Sampling, cases
are drawn from each stratum in same
proportion as they occur in the universe.
Disproportionate stratified sampling is also
known as equal size stratified sampling. In this
method, an "equal number" of cases are selected
from each stratum irrespective of the size of the
stratum in the universe.
15. Cluster Sampling
In cluster sampling the stratification is done in a manner that the groups are
heterogeneous in nature rather than homogenous. Here the elements are not
selected from each stratum as is done in stratified sampling, rather the
elements are obtained by taking a sample of group and not from within
groups.
That means that out of several clusters or groups, one, two or more number
of clusters are selected by simple or stratified random method and their
elements are studied.
16. Single Stage Cluster Sampling:
Entire cluster is selected randomly for sampling.
Two Stage Cluster Sampling:
Here first we randomly select clusters and then from those selected clusters we randomly select elements
for sampling
17. Advantage:
1. It is best used for widely geographically spread population.
2. It is time and cost efficient. In cluster sampling the cost per element is greatly reduced.
Disadvantage:
1. If cluster are not true representative of the population, then it can affect internal
validity of the study.
2. Sample can be bias and non-representative at times.
18. Multi-Stage Sampling
The method is used in selecting a sample from a very large area.
As the name suggests, It refers to a sampling technique which is carried out in various
stages.
Normally a multi-stage sampling is the one that combines cluster and random sampling
methods.
Population is divided into multiple clusters and then these clusters are further divided
and grouped into various sub groups (strata) based on similarity. One or more clusters can
be randomly selected from each stratum. This process continues until the cluster can’t be
divided anymore.
For example country can be divided into states, cities, urban and rural and all the areas
with similar characteristics can be merged together to form a strata.
19.
20. MERITS:-
1. It is a good representative of the population.
2. It is an improvement over the earlier methods.
3. It is useful while collecting primary data from a geographically dispersed
population.
4. Finding the right survey sample becomes very convenient for researchers.
DEMERITS:-
It is likely to cause a large number of errors as it involves a process of divisions
and sub-divisions of the various strata or clusters in different stages.
21. Non-Probability Sampling
It does not rely on randomization. This type of sampling is also known as non-random
sampling.
This technique is more reliant on the researcher’s ability to select elements for a sample.
Outcome of sampling might be biased and makes difficult for all the elements of
population to be part of the sample equally.
The major forms of non-probability samples are;
Convenience Sampling
Purposive Sampling
Quota Sampling
Referral /Snowball Sampling
22. Convenience Sampling:
Here the samples are selected based on the availability. This method is used when the
availability of sample is rare and also costly.
It is known as unsystematic, careless, accidental or opportunistic sampling. Under this a
sample is selected according to the convenience of the investigator.
May be use when:
❖ Universe is not clearly defined
❖ Sampling units are not clear
❖ Complete source list is not available
23. Purposive Sampling:
"Deliberate Sampling" or "Judgment Sampling”
It is selected by some arbitrary method because it is known to be
representative of the total population.
It is known that it will produce well-matched groups.
This is based on the intention or the purpose of study. Only those elements will
be selected from the population which suits the best for the purpose of our study.
24. The researcher chooses the sample based on who they think would be appropriate for the
study.
This is used primarily when there is a limited number of people that have expertise in the
area being researched
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.
25. Referral /Snowball Sampling:
This technique is used in the situations where the population is completely unknown and
rare.
Therefore we will take the help from the first element which we select for the population
and ask him to recommend other elements who will fit the description of the sample
needed.
So this referral technique goes on, increasing the size of population like a snowball.
Snowball sampling method is purely based on referrals and that is how a researcher is able
to generate a sample. Therefore this method is also called the chain-referral sampling
method.
This sampling technique can go on and on, just like a snowball increasing in size (in this
case the sample size) till the time a researcher has enough data to analyze, to draw
conclusive results that can help an organization make informed decisions.
26. For example: It’s used in situations of highly sensitive topics like HIV Aids where people
will not openly discuss and participate in surveys to share information about HIV Aids.
Not all the victims will respond to the questions asked so researchers can contact people
they know or volunteers to get in touch with the victims and collect information
27. Quota Sampling:
This type of sampling depends of some pre-set standard.
In such a process, the researcher decides the selection of sampling based on some
quota.
The researcher makes sure that the final sample must meet his quota criteria.
The population is classified into several categories. The proportion of population
falling into each category is decided on the basis of judgement or assumption or
the previous knowledge.
It selects the representative sample from the population. Proportion of characteristics/ trait
in sample should be same as population.
28.
29. Types of Quota Sampling:
•Controlled Quota Sampling
When a researcher or surveyor is confined to only a few sample options, this is referred to as a
Controlled Quota Sampling. For example, a school bag manufacturer would wish to conduct a
wide survey of kids' preferences for school bags. The research would be confined to students
who were in school at the time.
•Uncontrolled Quota Sampling
Uncontrolled Quota Sample is any circumstance in which the researcher or analyst has no limits
or limitations for the sampling procedure. A study undertaken by medical personnel to
understand the total public health and well-being in a nation-state at regular intervals is an
example of uncontrolled quota sampling. The research would include samples from people of all
ages, places of residence, gender, and other factors in the general population.
30. MERITS:-
1. Quota sampling is a cost-effective procedure. It saves both time and money by informing us about
how many samples of each group we need to gather.
2. It is an improvement over the judgement sampling.
3. Quota sampling is all about taking into account population proportions.
DEMERITS:-
1. Inaccuracy is possible as only the pre-determined traits of the population are taken into account the
final sample may not accurately represent other traits.
2. Bias can become a problem: In quota sampling, it’s generally left up to the researchers to decide who
is sampled. Unknowingly or not, they may select based on convenience, cost, or other biases.
31. CONCLUSION
In conclusion, it can be said that using a sample in research saves mainly on
money and time, if a suitable sampling strategy is used, appropriate sample size
selected and necessary precautions taken to reduce on sampling and measurement
errors, then a sample should yield valid and reliable information
