SAMPLING AND
SAMPLING METHODS
ABDULHAKIM H.(MPH)
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Introduction
Researchers often use sample survey
methodology to obtain information about a
larger population by selecting and measuring a
sample from that population.
Since population is too large, we rely on the
information collected from the sample.
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Introduction…
Usually, a representative subgroup of the
population (sample) is included in the
investigation.
A representative sample has all the important
characteristics of the population from which it
is drawn.
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Introduction…
Sampling enables us to estimate the characteristic of
a population by directly observing a portion of the
population.
Researchers are not interested in the sample itself,
but in what can be learned from the sample—and how this
information can be applied to the entire population.
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Introduction…
Inferences about the population are based
on the information from the sample drawn
from that population.
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Sampling
Sampling :-involves the selection of a number of a
study units from a defined population.
A main concern in sampling:
Ensure that the sample represents the population, and
the findings can be generalized
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Advantages of sampling
Feasibility: Sampling may be the only feasible method
of collecting information.
Reduced cost: Sampling reduces demands on resource
such as finance, personnel, and material.
Greater accuracy: Sampling may lead to better accuracy
of collecting data
Greater speed: Data can be collected and summarized
more quickly
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Sampling....
While selecting a SAMPLE, there are basic
questions:
What is the group of people from which we want to draw
a sample?
How many people do we need in our sample?
How will these people be selected?
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Common terms used in sampling
Reference population (source population / target
population)
the population of interest, to which the investigators
would like to generalize
the results of the study, and from which a representative
sample is to be drawn.
Study or sample population - the population
included in the sample.
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Common terms used in
sampling
Sampling unit - the unit of selection in the sampling
process. (group, individual/things)
Study unit - the unit on which information is collected.
Sampling frame - the list of all the units in the reference
population, from which a sample is to be picked.
Sampling fraction (Sampling interval) - the ratio of the
number of units in the sample to the number of units in
the reference population (n/N)
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Sampling Methods
Two broad divisions:
A. Probability sampling methods
B. Non-probability sampling methods
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A. Probability sampling
methods
A sampling frame exists or can be compiled.
Involve random selection procedures.
All units of the population should have an equal or at
least a known chance of being included in the sample.
Generalization is possible (from sample to population).
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Most common probability
sampling methods
1. Simple random sampling
2. Systematic random sampling
3. Stratified random sampling
4. Cluster sampling
5. Multi-stage sampling
6. Sampling with probability proportional to Size.
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1. Simple random sampling
The required number of individuals are selected at
random from the sampling frame, a list or a database
of all individuals in the population
Each member of a population has an equal chance of
being included in the sample.
Representativeness of the sample is ensured.
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1. Simple random sampling...
To use a SRS method:
Make a numbered list of all the units in the population
from which you want to draw a sample.
Each unit on the list should be numbered in sequence
from 1 to N (where N is the size of the population).
Decide on the size of the sample.
Select the required number of study units, using a
“lottery” method or a table of random numbers.
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1. Simple random sampling...
SRS has certain limitations:
Requires a sampling frame.
Difficult if the reference population is dispersed.
minority subgroups of interest in the population
may not be present in the sample in sufficient
numbers for study.
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2. Systematic random sampling
Individuals are chosen at regular intervals (
for Example, every kth) from the sampling
frame.
The first unit to be selected is taken at
random from among the first k units.
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Steps in systematic random sampling
1. Number the units on your frame from 1 to N (where N is
the total population size).
2. Determine the sampling interval (K) by dividing the
number of units in the sample by the total number of
reference population.
3. Select a number between one and K at random. This
number is called the random start and would be the first
number included in your sample.
4. Select every Kth unit after that first number
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Example
To select a sample of 100 from a student of 400, you
would need a sampling interval: 100 /400 = 1/4.
Hence, the sample interval (K)= 4.
The number of the first student to be included in the
sample is chosen randomly, for example by blindly
picking one out of four of pieces of paper, numbered
1 to 4.
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Example….
If you choose 3, the third unit on your frame
would be the first unit included in your sample;
The sample might consist of the following units
to make up a sample of 100: 3 (the random
start), 7, 11, 15, 19...395, 399 (up to N, which is
400 in this case).
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Steps in systematic random sampling
Merits
Systematic sampling is usually less time consuming and
easier to perform than simple random sampling.
Demerits
If there is any sort of cyclic pattern in the ordering
of the subjects which coincides with the sampling
interval, the sample will not be representative of the
population.
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3. Stratified random sampling
It is appropriate when the distribution of the
characteristic to be studied is strongly affected by
certain variable (heterogeneous population).
The population is first divided into groups (strata)
according to a characteristic of interest (eg., age, sex,
geographic area, prevalence of Disease,etc.).
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3. Stratified random sampling…
A separate sample is then taken independently from
each stratum, by simple random or systematic
sampling.
When a population is stratified, each stratum becomes
an independent population and you will need to decide
the sample size for each stratum.
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3. Stratified random sampling..
proportional allocation - if the same sampling
fraction is used for each stratum.
Proportionate allocation
nj = n/N*Nj
– nj is sample size of the jth stratum
– Nj is population size of the jth stratum
– n = n1 + n2 + ...+ nk is the total sample size
– N = N1 + N2 + ...+ Nk is the total population size
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3. Stratified random sampling..
Example: Proportionate Allocation
Village A B C D Total
HHs 100 150 120 130 500
S. size ? ? ? ? 60
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3. Stratified random sampling..
Merit
The representativeness of the sample is
improved.
DEMERIT
Sampling frame for the entire population has to
be prepared separately for each stratum.
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4. Cluster sampling
Cluster sampling is the most widely used to
reduce the cost.
In this sampling scheme, selection of the
required sample is done on groups of study
units (clusters) instead of each study unit
individually.
The sampling unit is a cluster, and the sampling
frame is a list of these clusters.
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Steps in cluster sampling
The reference population (homogeneous) is
divided into clusters.
A sample of such clusters is selected randomly.
then all units within selected clusters are
included in the sample.
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Example
In a school based study, we assume students of the
same school are homogeneous.
We can select randomly sections and include all
students of the selected sections only
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4. Cluster sampling
Merit
A list of all the individual study units in the reference population
is not required.
It is sufficient to have a list of clusters.
Demerit
It is based on the assumption that the characteristic to be
studied is uniformly distributed throughout the reference
population, which may not always be the case.
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5. Multi-stage sampling
This method is appropriate when the reference
population is large and widely scattered.
Similar to the cluster sampling, except that it involves
picking a sample from within each chosen cluster,
rather than including all units in the cluster.
This type of sampling requires at least two stages.
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5. Multi-stage sampling
The primary sampling unit (PSU) is the sampling
unit in the first sampling stage.
The secondary sampling unit (SSU) is the
sampling unit in the second sampling stage, etc.
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Example:
Woreda PSU
Kebele SSU
Sub-Kebele TSU
HH
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5. Multi-stage sampling …..
• In the first stage, large groups or clusters are identified
and selected. These clusters contain more population
units than are needed for the final sample.
In the second stage, population units are picked from
within the selected clusters (using any of the possible
probability sampling methods) for a final sample.
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5. Multi-stage sampling …..
If more than two stages are used, the process of
choosing population units within clusters
continues until there is a final sample.
Merit:- reduce the cost and time of preparing
sampling frame.
Demerit:- Sampling error is increased compared
with a simple random sample.
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B. Non-probability sampling
In non-probability sampling, every item has an
unknown chance of being selected.
In non-probability sampling, there is
an assumption that there is an even distribution of
a characteristic of interest within the population.
For probability sampling, random is a feature of
the selection process.
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B. Non-probability sampling...
In non-probability sampling, since elements are
chosen arbitrarily, there is no way to estimate
the probability of any one element being
included in the sample.
Also, no assurance is given that each item has a
chance of being included, making it impossible
either to estimate sampling variability or to
identify possible bias
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B. Non-probability sampling...
Despite these drawbacks, non-probability sampling
methods can be useful when descriptive comments
about the sample itself are desired.
Secondly, they are quick, inexpensive and convenient.
There are also other circumstances, such as
researches, when it is unfeasible or impractical to
conduct probability sampling.
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The most common types of non-
probability sampling
1. Convenience or haphazard sampling
2. Quota sampling
3. Snowball sampling technique
4. Volunteer sampling
5. Judgment sampling
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1. Convenience or haphazard sampling
is a method in which for convenience sake the
study units that happen to be available at the
time of data collection are selected.
It is not normally representative of the target
population because sample units are only
selected if they can be accessed easily and
conveniently.
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1. Convenience or haphazard sampling
The obvious advantage is that the method is
easy to use, but that advantage is greatly offset
by the presence of bias.
Although useful applications of the technique
are limited, it can deliver accurate results when
the population is homogeneous.
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2. Quota sampling
This is one of the most common forms of non-
probability sampling.
Sampling is done until a specific number of units
(quotas) for various sub-populations have been
selected.
Since there are no rules as to how these quotas are
to be filled, quota sampling is really a means for
satisfying sample size objectives for certain sub-
populations.
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2. Quota sampling…
The main argument against quota sampling is
that it does not meet the basic requirement of
randomness.
Some units may have no chance of selection or
the chance of selection may be unknown.
Therefore, the sample may be biased
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2.Quota sampling….
Quota sampling
is generally less expensive than random sampling.
is also easy to administer, especially considering the
tasks of listing the whole population, randomly
selecting the sample and following-up on non-
respondents can be omitted from the procedure.
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3. Snowball sampling
A technique for selecting a research sample
where existing study subjects recruit future
subjects from among their acquaintances
(friends).
Thus the sample group appears to grow like a
rolling snowball.
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3. Snowball sampling…
This sampling technique is often used in hidden
populations which are difficult for researchers to access;
Example populations would be drug users or commercial
sex workers.
Because sample members are not selected from a
sampling frame,
snowball samples are subject to numerous
biases.
For example, people who have many friends are more likely to be
recruited into the sample.
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4. Volunteer sampling
• As the term implies, this type of sampling occurs when people
volunteer to be involved in the study.
• In psychological experiments or pharmaceutical trials (drug
testing), for example, it would be difficult and unethical to
enlist random participants from the general public.
• In these instances, the sample is taken from a group of
volunteers.
Sometimes, the researcher offers payment to attract
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• In exchange, the volunteers accept the possibility of a
lengthy, demanding or sometimes unpleasant process.
• Sampling voluntary participants as opposed to the
general population may introduce strong biases.
• Often in opinion polling, only the people who care
strongly enough about the subject tend to respond.
• The silent majority does not typically respond,
resulting in large selection bias.
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5. Judgment sampling
• This approach is used when a sample is taken based
on certain judgments about the overall population.
• The underlying assumption is that the investigator will
select units that are characteristic of the population.
• The critical issue here is objectivity: how much can
judgment be relied upon to arrive at a typical sample?
• Judgment sampling is subject to the researcher's
biases.
• One advantage of judgment sampling is the reduced
cost and time involved in acquiring the sample.
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Thank you
