SAMPLING
METHODS
Prepared by:
Dr. Satish Kumar Yadav
MPH, 1st Sem (2021 Batch)
SPH & CM, BPKIHS

Population: is the entire group that we want to draw
conclusions about.
Sample: is the specific group that we will collect data from. It
represents the characteristics of population.
The size of the sample is always less than the total size of the
population.
● In research, a population
doesn’t always refer to
people.
● It can mean a group
containing elements of
anything we want to study,
such as objects, events,
organizations, countries,
species, organisms, etc.

Sampling:- is the selection of a subset (a statistical sample) of
individuals from within a statistical population to estimate
characteristics of the whole population.
When the population is large in
size, geographically dispersed, or
difficult to contact, it’s necessary
to use a sample.
Two advantages of sampling are:
1. Lower cost and
2. faster data collection (than measuring the
entire population.)

Types of Sampling
Random Sampling Non-Random Sampling
Also k/a Probability sampling
1. Simple Random Sampling
2. Systemic Random Sampling
3. Stratified Random Sampling
4. Multistage Random Sampling
5. Multiphase Random Sampling
6. Cluster Random Sampling
Also k/a Non-Probability sampling
1. Convenience Sampling
2. Quota Sampling
3. Snow-ball Sampling
4. Judgemental Sampling

Probability Sampling
Methods
Non-Probability Sampling
Methods
Definition
Probability Sampling is a sampling
technique in which samples from a
larger population are chosen using a
method based on the theory of
probability.
Non-probability sampling is a sampling
technique in which the researcher selects
samples based on the researcher’s
subjective judgment rather than random
selection.
Nature The research is conclusive. The research is exploratory.
Sample
Since there is a method for deciding
the sample, the population
demographics are conclusively
represented.
Since the sampling method is arbitrary, the
population demographics representation is
almost always skewed.
Time
Taken
Takes longer to conduct since the
research design defines the selection
parameters before the research study
begins.
This type of sampling method is quick since
neither the sample or selection criteria of
the sample are undefined.
Results
This type of sampling is entirely
unbiased and hence the results are
unbiased too and conclusive.
This type of sampling is entirely biased and
hence the results are biased too, rendering
the research speculative.
Hypothesi
In probability sampling, there is an
underlying hypothesis before the study
In non-probability sampling, the hypothesis
is derived after conducting the research

SIMPLE RANDOM SAMPLING
- Every unit of population has equal and known chance of being selected.
- Is also known as “unrestricted random sampling”
- Applicable for small, homogenous and readily available populations
- Used in clinical trials
- Methods of simple random sampling:
- lottery method
- random number tables
- computer softwares

SYSTEMIC RANDOM SAMPLING
- Based on sampling fraction: every ‘K’ th unit is chosen in the population
list, where K is chosen by sampling interval.
- Sampling interval (K) Q = Total no. of units in population / Total no. of units
in sample
- For example, if there is a population of 1000 from which sample of 20 is
to be chosen, then K=1000/20 = 50; thus EVERY 50TH UNIT WILL BE
INCLUDED IN THE SAMPLE (1st, 51st, 101st, so on..) First unit among the 50
is chosen by simple random sampling.
- Applicable for large,
non-homogenous
populations where
complete list of
individual is
available.

STRATIFIED RANDOM SAMPLING
- Non-homogenous population is converted to homogenous groups/classes (stata);
sample is drawn from each strata at random, in proportion to its size
For example, in a population of 1000, sample of 100 is to be drawn for hemoglobin
estimation; first non-homogenous population is converted to homogenous strata (i.e.
700 males and 300 females), then draw 70 males and 30 females randomly respectively.
- Applicable for large non-
homogenous population.
- Gives more representative
sample than simple random
sampling.
- None of the categories is
under or over represented

Cluster Sampling
 Cluster sampling is an example of two stage sampling
(first stage sample of area is chosen, second area a sample or respondent within the
area)
 A cluster sample is obtained by selecting clusters from the population on the basis of simple
random sampling
 The sample comprises a census of each random cluster selected, sample unit is group rather
than individual
 This method is used when the unit of population are natural groups or clusters such as villages,
wards, blocks, children of school etc. in which every unit of cluster is taken.
 Used to study large populations, particularly those that are widely geographically dispersed.
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Multistage Sampling
 Complex form of cluster sampling in which two or more levels of units are embedded one in the other
 Sampling procedures carried out in several stages using random sampling techniques
 Done for large county surveys
 First stage, random number of districts chosen in all province.
 Followed by random number of municipalities/rural municipalities .
 Then third stage units will be houses.
 At the last step, households and individuals within household are randomly selected
 Part of the information collected from whole sample & part from sub-sample.
 In TB survey MT in all cases – Phase I
 X –Ray chest in MT +ve cases – Phase II
 Sputum examination in X-Ray +ve cases - Phase III
Survey by such procedure is less costly, less laborious & more purposeful
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Non-Probability Sampling
 Each element of population has no equal chance of being selected of the item from the population
 If the sampling frame is not available, non-probability sampling technique is used.
 Using this technique, we may or may not represent the population well.
 Within the probability sampling we are using in between.
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Advantages
 Useful when the sample size is small
 Applied when the number of elements in the population is unknown.
Limitations
 There are high chances of selection bias
 It is not a scientific method
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Convenience Sampling
 Convenience sampling entails using most conveniently available people as participants.
Example, a researcher selects a sample as to those who appear in the hospital.
 Advantages
- It is useful for pre-testing questionnaires
- It is useful for pilot studies
 Limitations
- Selected samples might be a typical to the population
- There are high chances of selection bias
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Snowball Sampling
 Snowball sampling is used to reach target population where the sampling units are difficult to
identify.
 Under snowball sampling, each identified member of the target population is asked to identify
other sampling units who belong to the same target population.
 Snowball sampling would be used to identify successive sampling units, for example drug addicts,
sex workers, etc.
 The issues under investigation are usually confidential or sensitive in nature.
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Quota Sampling
 Is more suitable under the condition when sample quotas have been fixed according to some
specified characteristics ( e.g; age, income, education, sex, colors etc.)
 This technique is similar to stratified random sampling, however instead of randomly sampling
from each stratum, the researcher uses a non-random sampling method to gather data from one
stratum until the desired quota of samples is filled.
 Thank you
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