This document discusses various sampling methods used in research. It defines population and sample, and describes probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability sampling methods such as convenience sampling, judgmental sampling, snowball sampling, and quota sampling. The key points are that probability sampling aims for representativeness while non-probability sampling does not, and the appropriate method depends on the research goals and population characteristics.

1. Sampling
Dr. Kshitija Gandhi
PHD, MPHIL, MCOM,MBA,UGC NET
Vice Principal
Pratibha College of
Commerce and Computer studies

2. Population vs
Sample
• The population is the entire group that you want to draw
conclusions about.
• The sample is the specific group of individuals that you
will collect data from.
• In research, a population doesn’t always refer to people.
It can mean a group containing elements of anything you
want to study, such as objects, events, organizations,
countries, species, organisms, etc.

3. Population vs
Sample
Population Sample
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the India
The top 50 search results for
advertisements for IT jobs in
the India on May 1, 2020
Undergraduate students in the
Netherlands
300 undergraduate students
from three Dutch universities
who volunteer for your
psychology research study
All countries of the world Countries with published data
available on birth rates and
GDP since 2000

4. Population
• To make inferences about the whole adult population of your country
• Customers of a certain company
• Patients with a specific health condition, or students in a single school.
• It is important to carefully define your target population according to the purpose
and practicalities of your project.
• If the population is very large, demographically mixed, and geographically
dispersed, it might be difficult to gain access to a representative sample.
The population can be defined in terms of geographical
location, age, income, and many other characteristics.

5. Population
• Definition - a complete set of elements (persons or objects) that possess
some common characteristic defined by the sampling criteria
established by the researcher
• Composed of two groups - Target population & Accessible population

6. Target
population
(Universe)
• All institutionalized elderly with Alzheimer’s
• All people with AIDS
• All low birth weight infants
• All school-age children with asthma
• All pregnant teens
The entire group of people or objects to which the researcher
wishes to generalize the study findings meet set of criteria of
interest to researcher

7. Accessible population
• The portion of the population
to which the researcher has
reasonable access; may be a
subset of the target population
• May be limited to region, state,
city, county, or institution
All institutionalized elderly with Alzheimer's in St. Louis
county nursing homes
All people with AIDS in the metropolitan St. Louis area
All low birth weight infants admitted to the neonatal ICUs
in St. Louis city & county
All school-age children with asthma treated in pediatric
asthma clinics in university-affiliated medical centers in
the Midwest
All pregnant teens in the state of Missouri

8. Samples • Sample = the selected elements (people or objects) chosen
for participation in a study; people are referred to as subjects
or participants
• Sampling = the process of selecting a group of people, events,
behaviors, or other elements with which to conduct a study
• Sampling frame = a list of all the elements in the population
from which the sample is drawn
• Could be extremely large if population is national or
international in nature

9. • Frame is needed so that everyone in
the population is identified so they
will have an equal opportunity for
selection as a subject (element)
A list of all institutionalized elderly with Alzheimer's in
St. Louis county nursing homes affiliated with BJC
A list of all people with AIDS in the metropolitan St.
Louis area who are members of the St. Louis Effort for
AIDS
A list of all low birth weight infants admitted to the
neonatal ICUs in St. Louis city & county in 1998
A list of all school-age children with asthma treated in
pediatric asthma clinics in university-affiliated medical
centers in the Midwest
A list of all pregnant teens in the Henderson school
district

10. Randomization = each individual in the population has an equal opportunity
to be selected for the sample
Representativeness = sample must be as much like the population in as
many ways as possible
Sample reflects the characteristics of the population, so those sample
findings can be generalized to the population
Most effective way to achieve representativeness is through
randomization; random selection or random assignment
Parameter = a numerical value or measure of a characteristic of the
population; remember P for parameter & population
Statistic = numerical value or measure of a characteristic of the
sample; remember S for sample & statistic
Precision = the accuracy with which the population parameters have
been estimated; remember that population parameters often are based on
the sample statistics

11. Sampling frame
The sampling frame is the actual
list of individuals that the sample
will be drawn from. Ideally, it
should include the entire target
population (and nobody who is not
part of that population).
You are doing research on working
conditions at Company X. Your
population is all 1000 employees of
the company. Your sampling frame
is the company’s HR database
which lists the names and contact
details of every employee.

12. Sample size
The number of individuals in your
sample depends on the size of the
population, and on how precisely you
want the results to represent the
population as a whole.
The larger the sample size,
the more accurately and
confidently you can make
inferences about the whole
population.

13. Probability
sampling
methods
Probability sampling means
that every member of the
population has a chance of
being selected.
It is mainly used in quantitative
research. If you want to
produce results that are
representative of the whole
population, you need to use a
probability sampling technique.

14. Probability sampling methods

15. Simple random sampling
• In a simple random sample, every
member of the population has an
equal chance of being selected.
• Your sampling frame should include
the whole population.
• To conduct this type of sampling,
you can use tools like random
number generators or other
techniques that are based entirely
on chance.

16. Simple
random
sampling
You want to select a simple random sample
of 100 employees of Company X. You assign
a number to every employee in the company
database from 1 to 1000 and use a random
number generator to select 100 numbers.

17. Systematic
sampling
• Systematic sampling is similar
to simple random sampling.
• But it is usually slightly easier
to conduct.
• Every member of the
population is listed with a
number
• But instead of randomly
generating numbers,
individuals are chosen at
regular intervals.

18. Systemati
c
Sampling
Example
All employees of the company are listed in
alphabetical order. From the first 10 numbers,
you randomly select a starting point: number 6.
From number 6 onwards, every 10th person on
the list is selected (6, 16, 26, 36, and so on),
and you end up with a sample of 100 people.
If you use this technique, it is important to make sure
that there is no hidden pattern in the list that might
skew the sample. For example, if the HR database
groups employees by team, and team members are
listed in order of seniority, there is a risk that your
interval might skip over people in junior roles,
resulting in a sample that is skewed towards senior
employees.

19. Stratified sampling
• Stratified sampling involves dividing the population into
subpopulations that may differ in important ways. It allows you
draw more precise conclusions by ensuring that every subgroup
is properly represented in the sample.
• To use this sampling method, you divide the population into
subgroups (called strata) based on the relevant characteristic
(e.g. gender, age range, income bracket, job role).
• Based on the overall proportions of the population, you
calculate how many people should be sampled from each
subgroup. Then you use random or systematic sampling to
select a sample from each subgroup.

20. • Example
• The company has 800 female employees and 200 male
employees. You want to ensure that the sample reflects the
gender balance of the company, so you sort the population into
two strata based on gender. Then you use random sampling on
each group, selecting 80 women and 20 men, which gives you
a representative sample of 100 people.

21. Cluster sampling
• Cluster sampling also involves dividing the population into
subgroups, but each subgroup should have similar characteristics to
the whole sample. Instead of sampling individuals from each
subgroup, you randomly select entire subgroups.
• If it is practically possible, you might include every individual from
each sampled cluster. If the clusters themselves are large, you can
also sample individuals from within each cluster using one of the
techniques above.
• This method is good for dealing with large and dispersed
populations, but there is more risk of error in the sample, as there
could be substantial differences between clusters. It’s difficult to
guarantee that the sampled clusters are really representative of the
whole population.

22. • Thank

23. Non Probability Sampling
• Non-probability sampling is a sampling technique where the
samples are gathered in a process that does not give all the
individuals in the population equal chances of being selected.
• True random sampling is always difficult to achieve.
• Time, money and workforce and because of these limitations.
• Non-probability sample is not a product of a randomized
selection processes.
• Subjects in a non-probability sample are usually selected on the
basis of their accessibility or by the purposive personal judgment
of the researcher.
• He downside of the non-probability sampling method is that an
unknown proportion of the entire population was not sampled
• He downside of the non-probability sampling method is that an
unknown proportion of the entire population was not sampled
The downside of the non-probablity sampling method is that an unknown proportion of the entire population was not sampled. This entails that the sample may or may not represent the entire population accurately. Th

24. When to Use Non-
Probability Sampling
To demonstrate that a particular
trait exists in the population.
It is used when the researcher aims to do
a qualitative, pilot or exploratory study.
It is used when randomization is impossible
like when the population is almost limitless.
It is used when the research does not aim to
generate results that will be used to
create generalizations pertaining to the
entire population.
It is when the researcher has limited budget,
time and workforce.
This technique is used in an initial study
which will be carried out again using a
randomized, probability sampling.
Check out our quiz-page with tests about:

25. Convenience Sampling :
• Convenience sampling is a non-
probability sampling technique where
subjects are selected because of
their convenient accessibility and
proximity to the researcher.
•
Most common
• Accessible to Researcher
• Choice of Subjects based
on easiness
• Easiest, cheapest, Least
time consuming

26. Examples
Pilot Study
useful in documenting that a
particular quality of a substance or
phenomenon occurs within a given
sample.
useful for detecting relationships
among different phenomena.

27. Criticism
sampling bias : the sample is not
representative of the entire population.
Systematic bias stems from sampling bias.
This refers to a constant difference between
the results from the sample and the
theoretical results from the entire
population.
Low external validity of the study.

28. Sequential
Sampling
Consecutive sampling is very
similar to convenience sampling
except that it seeks to include ALL
accessible subjects as part of the
sample. This non-probability
sampling technique can be
considered as the best of all non-
probability samples because it
includes all subjects that are
available that makes the sample a
better representation of the entire
population.

29. Sequential Sampling
This sampling technique gives the researcher
limitless chances of fine tuning his research
methods and gaining a vital insight into the study
that he is currently pursuing.

30. Advantages
The researcher has a limitless option when it
comes to sample size and sampling schedule.
The sample size can be relatively small of
excessively large depending on the decision
making of the researcher.
Sampling schedule is also completely dependent
to the researcher since a second group
of samples can only be obtained after conducting
the experiment to the initial group of samples.

31. Disadvantages
Hardly representative of
the entire population.
Hardly randomized.

32. Quota
Sampling
• Quota sampling is a non-probability sampling
technique
• It ensures equal or proportionate
representation of subjects depending on
which trait is considered as basis of the
quota.
• For example, if basis of the quota is college
year level and the researcher needs equal
representation, with a sample size of 100, he
must select 25 1st year students, another 25
2nd year students, 25 3rd year and 25 4th
year students. The bases of the quota are
usually age, gender, education, race, religion
and socioeconomic status.

33. Step-by-
step Quota
Sampling
• The first step is to divide the population into
exclusive subgroups.
• Identify the proportions of these subgroups in
the population.
• Same proportion will be applied in
the sampling process.
• Selects subjects from the various subgroups
while taking into consideration the
proportions noted in the previous step.
• The final step is representative of the entire
population.
• It is used to study traits and characteristics
that are noted for each subgroup.

34. When to
Use Quota
Samples
• Aim is to investigate a trait or a
characteristic of a certain subgroup, this
type of sampling is the ideal technique.
• To observe relationships between
subgroups.
• Traits of a certain subgroup interact with
other traits of another subgroup.
• To use this type of sampling technique.

35. Judgmental
Sampling
Judgmental sampling : known as purposive sampling.
subjects are chosen to be part of the sample with a
specific purpose in mind
where the researcher selects units to be sampled
based on their knowledge and professional judgment.

36. When to
Use
Judgment
al
Sampling
Judgmental sampling
design is usually used
when a limited
number of individuals
possess the trait of
interest.
It is the only viable
sampling technique in
obtaining information
from a very specific
group of people.
It is also possible to
use judgmental
sampling if the
researcher knows a
reliable professional
or authority that he
thinks is capable of
assembling a
representative
sample.

37. Snowball Sampling
• Snowball sampling is usually done
when there is a very small
population size.
• In this type of sampling, the
researcher asks the initial subject to
identify another potential subject
who also meets the criteria of the
research.
• The downside of using a snowball
sample is that it is hardly
representative of the population.

38. Process of
Snowball
Sampling
• The process of snowball sampling is much
like asking your subjects to nominate another
person with the same trait as your next
subject.
• The researcher then observes the nominated
subjects
• Continues in the same way until the
obtaining sufficient number of subjects.

39. Process of Snowball
Sampling
• For example, if obtaining subjects for a study that wants to observe
a rare disease, the researcher may opt to use snowball sampling
since it will be difficult to obtain subjects. It is also possible that the
patients with the same disease have a support group; being able to
observe one of the members as your initial subject will then lead
you to more subjects for the study.

40. References
• https://explorable.com/non-probability-
sampling
• https://www.scribbr.com/methodology/sa
mpling-methods/