An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..

1. Alam Nuzhathalam1 Associate Professor
Nuzhath Alam

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 Sampling
 Introduction, terminologies
 Definition, characteristics of good sample
 Sampling criteria, factors influencing sampling process
 Sampling Steps/Design/Process
 Sampling Methods/Techniques/Types
 Probability Sampling
 Non-Probability Sampling
 Sampling Errors and Non-Sampling Errors

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4.  Population: The entire set of individuals or objects having
some common characteristics selected for a research study
 Target Population: Entire group of people or objects to
which the researcher wishes to generalize the findings of
the study
 Sample: A subset of the population study population
target population sample
 Study Population (Sampling): The population to be
studied/to which the investigator wants to generalize his
results
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5.  Sampling Unit: Smallest unit from which sample can be
selected
 Sampling Frame: List of all the sampling units from which
sample is drawn
 Sampling Scheme: Method of selecting sampling units
from sampling frame
 Sampling Fraction: Ratio between sample size and
population size
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7. Definition:
 Sampling is the process of selecting units (e.g., people,
organizations) from a population of interest so that by
studying the sample we may fairly generalize our results
back to the population from which they were chosen
 The process by which researchers select a representative
subset or part of the total population that could be
studied for their topic so that they will be able to draw
conclusions about the entire population
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8.  A sample should be reliable
 A sample should be economical
 A sample should be proportional
 A sample should be goal oriented
 A sample should be appropriate in size
 A sample should be selected at randomly
 A sample should be free from bias and errors
 A sample should be true representation of population
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9.  Sampling criteria refers to the essential characteristics
of a subject or respondent such as ability to read and
write responses on the data collection instruments
 For example, these criteria could include:
 Age (elderly, children, etc)
 Gender (male/female)
 Marital status
 Ability to understand English/Hindi, etc
 Ability to write
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10. Factors that influence sampling process:
 Element Type
 Research Type
 Population size
 Available Resources
 Constraints/ limitations
 Participation (response)
 When might you sample the entire population?
 When your population is very small, When you have
extensive resources, When you don’t expect a very high
response
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12. SSCSM: Soft Skill Computer
Shaksharta Mission
 S - Simple
 S - Stratified
 C - Cluster
 S - Systematic
 M - Multi Stage
CQPSS: Continuous Quality
Process Software & Systems
 C - Convenience
 Q - Quota
 P - Purposive
 S - Self Selection
 S - Snow Ball
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Probability Sampling Non-Probability Sampling

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14.  Probability sampling is defined as a sampling technique
in which the researcher chooses samples from a larger
population using a method based on the theory of
probability. For a participant to be considered as a
probability sample, he/she must be selected using a
random selection
 Probability sampling uses statistical theory to randomly
select a small group of people (sample) from an existing
large population and then predict that all their responses
will match the overall population
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 Simple random sampling is a sampling technique where
every item in the population has an even chance and
likelihood of being selected in the sample. Here the selection
of items completely depends on chance and therefore this
sampling technique is also sometimes known as a method of
chances (i.e., Lottery method, random table)
 Example: A simple random sample would be the names of
25 employees being chosen out of a hat from a company of
250 employees. In this case, the population is all 250
employees, and the sample is random because each
employee has an equal chance of being chosen

18. Advantages:
 It is a fair method of sampling and if applied appropriately it
helps to reduce any bias involved as compared to any other
sampling method involved
 Since it involves a large sample frame it is usually easy to pick
smaller sample size from the existing larger population
Disadvantages:
 It is a costlier method of sampling as it requires a complete list
of all potential respondents to be available beforehand
 This sampling method is not suitable for studies involving face-
to-face interviews as covering large geographical areas have
cost and time constraints

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20.  Stratified sampling is a probability sampling technique
wherein the researcher divides the entire population into
different subgroups or strata, then randomly selects the
final subjects proportionally from the different strata
 A stratified sample is one that ensures that subgroups
(strata) of a given population are each adequately
represented within the whole sample population of
a research study. Example: one might divide a sample of
adults into subgroups by age, like 15–19, 20–24, 25–29,
30–34, and 35 and above
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21. Advantages:
 Assures representation of all groups in sample population
 Characteristics of all groups in same population
Disadvantages:
 Requires accurate information on proportions of each
stratum
 Stratified lists costly to prepare
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23.  Cluster sampling refers to a type of sampling method.
With cluster sampling, the researcher divides the
population into separate groups, called clusters. Then, a
simple random sample of clusters is selected from the
population. The researcher conducts his analysis on data
from the sampled clusters
 For example, a researcher wants to survey academic
performance of nursing students in India. He can divide
the entire population into different cities. Then the
researcher selects a number of clusters depending on his
research through simple or systematic random sampling
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24. Advantages:
 Requires fewer resources; Since cluster sampling selects only
certain groups from the entire population, the method
requires fewer resources for the sampling process
 More feasible; The division of the entire population into
homogenous groups increases the feasibility of the sampling
Disadvantages:
 The cost to reach an element to sample is very high
 Each stage in cluster sampling introduces sample error. The
more stages there are more error tends to be
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26.  Systematic sampling is a type of probability sampling
method in which sample members from a larger
population are selected according to a random starting
point but with a fixed, periodic interval. This interval,
called the sampling interval, is calculated by dividing the
population size by the desired sample size
 For example, if a researcher is seeking to form a
systematic sample of 50 volunteers from a nursing
students of 500, they can select every 10th student to
build a sample systematically
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27. Advantages:
 It’s extremely simple and convenient for the researchers to
create, conduct, analyze samples
 As there’s no need to number each member of a sample,
it is better for representing a population in a faster and
simpler manner
Disadvantages:
 Periodic ordering required
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29.  Multistage sampling is defined as a sampling method that
divides the population into groups (or clusters) for
conducting research. During this sampling method, significant
clusters of the selected people are split into sub-groups at
various stages to make it simpler for primary data collection
 Example: Stratify the population by region of the country.
For each region, stratify by urban, suburban, and rural and
take a random sample of communities within those strata.
Divide the selected communities into city blocks as clusters,
and sample some blocks. Everyone on the block or within the
fixed area may then be sampled
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30. Advantages:
 More accurate than the cluster sampling for same size
population
 Less time consuming
Disadvantages:
 Costly
 Not as accurate as simple random sampling
 Each stage in sampling introduces sample error. The more
stages there are more error tends to be
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31.  Population divided into
few groups
 Homogeneity within sub-
groups
 Heterogeneity between
sub-groups
 Choice of elements from
within sub-groups
 Population divided into
many groups
 Heterogeneity within sub-
groups
 Homogeneity between
sub-groups
 Random choice of sub-
groups
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32.  Non-probability sampling is a sampling method in which not
all members of the population have an equal chance of
participating in the study
 It is most useful for exploratory studies like a pilot survey.
Researchers use this method in studies where it is not
possible to draw random probability sampling due to time
or cost considerations
 It is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the
researcher rather than random selection. It is a less stringent
method
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 Convenience sampling (grab sampling, accidental sampling,
or opportunity sampling) is a type of non-probability
sampling that involves the sample being drawn from that
part of the population that is close to hand. This type of
sampling is most useful for pilot testing. Convenience
sampling involves choosing respondents all the convenience
of the researcher
 A convenience sample is a type of non-probability sampling
method where the sample is taken from a group of people
easy to contact or to reach. Example is using subjects that
are selected from a clinic, a class or an institution that is
easily accessible to the researcher

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Advantages:
 Quick
 Convenient
 Economical
 Extensivelyused
Disadvantages:
 Restriction of Generalization
 Sample may not be representative
 Projecting data beyond sample not justified
 Variability and bias cannot be measured or controlled

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 Quota sampling is a method for selecting survey participants
that is a non-probabilistic version of stratified sampling. The
population is first segmented into mutually exclusive sub-groups, just
asin stratified sampling
 In quota sampling, a population is first segmented into mutually
exclusive sub-groups, just as in stratified sampling. Then
judgment is used to select the subjects or units from each
segment based on a specified proportion
 Example, interviewers might be tempted to interview those
people in the street who look most helpful, or may choose to
use accidental sampling to question those closest to them, to
save time

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Advantages:
 Used when research budget is limited
 No need for list of population elements
 Easy to carry out than stratifies sampling
Disadvantages:
 Time consuming
 Selection of sample upon accessibility, prone to bias

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 Purposive sampling, also known as judgmental, selective,
or subjective sampling, is a form of non-
probability sampling in which researchers rely on their
own judgment when choosing members of the population
to participate in their study
 Participants are selected according to the needs of the
study. Applicants who do not meet the profile are
rejected. For example, Conducting a study on why high
school students choose community college over university

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Advantages:
 Low cost
 Less time involved
 Meet the specific objective
 There is a assurance of quality response
Disadvantages:
 Time consuming process
 Bias selection of sample may occur

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 Self-selection sampling (volunteer sampling) is a non-
probability technique, that is based on the judgement of
the researcher. This is a useful tool for researchers, who
want people or organizations, to participate as part of
a study on their own accord
 As a sampling strategy, self section sampling can be used
with a wide range of research designs
and research methods
 Example, Survey researchers may put a questionnaire
online and subsequently invite anyone within a particular
organisation to take part

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Advantages:
 More accurate
 Access to a variety of participants
 Useful in specific circumstances to serve the purpose
Disadvantages:
 Massare left
 Volunteer based
 More costly due to Advertizing

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 Snowball sampling is also known as chain sampling,
chain-referral sampling, referral sampling. It is a non-
probability sampling technique where existing study
subjects recruit future subjects from among their
acquaintances
 The research starts with a key person and introduce the
next one to becomeachain

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Advantages:
 Lowcost
 Locate hidden population
 Useful in specific circumstances & for locating rare populations
Disadvantages:
 Not Random
 Not independent
 Projecting data beyond sample not justified

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 Error caused by act of taking sample
 Sampling error is the error caused by observing a
sample instead of the whole population. The sampling
error is the difference between a sample statistic used to
estimate a population parameter and the actual but
unknown value of the parameter
Twotypes of samplingerrors
 Biased Errors: Due to selection of sampling technique; size of
the sample
 Unbiased Errors/Random sampling errors: Differences between
the members of the population included or notincluded

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Sampling errors can be reduced by the following methods:
 Increase the sample size. A larger sample size leads to a
more precise result because the study gets closer to the
actual population size
 Randomize selection to eliminate bias
 Divide the population into groups
 Perform an external record check
 Know your population
 Train your team

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 Any error or inaccuracies caused by factors other
than sampling error
 Non-sampling errors to biases and mistakes in selection of
sample
Examples of non-sampling errors are:
 Selection bias
 Interviewer error
 Population mis-specification error
 Respondent error, non-response error
 Sampling frame error, processing error

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Causes for non-sampling error:
 Sample size
 Processing error
 Loaded questions
 Lack of knowledge
 Sampling operations
 Inadequate of response
 Concealment of the truth
 Misunderstanding the concept

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 Probability sampling can generalize to population
 Probability sampling should be attempted as it has lowest bias
and more importantly significance of the result
 Non-probability sampling can be generalize to the institution
or place where the sample was studied
 Using a sample in research saves mainly on money and time, if
a suitable sample strategy is used appropriate size selected
and necessary precautions to reduce on sampling and
measurement errors, then a sample should yield valid and
reliable information

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