Sampling Techniques
Ph.D. Course work Lectures
Sampling Terms
 Population
 The entire group
of people/objects of
interest from whom the researcher needs
to obtain information.
 A population can be defined as set or
collection all people or items/objects with
the characteristic one wishes to
study/understand.
 It depends on the objective of your research
 It should be identified properly
 Appropriate inclusion-exclusion criterion
should be identified
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 An
element/unit is an object on which a
measurement is taken.
A
population is a collection of elements
about which we wish to make an
inference.
 Sampling
units are non-overlapping
collection of elements from the
population that cover the entire
population.
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A
sampling frame is a list of sampling units.
A
sample is a collection of sampling units
drawn from a sampling frame.
 Parameter: numerical
characteristic of a
population,like population
mean,variance,correlation etc.
 Statistic: numerical
characteristic of a
sample
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A
census study occurs if the entire
population is included in the study. In
census study, information is collected from
every unit/element of the population.
 Because there is not enough time or money
to gather information from everyone or
everything in a population, the goal
becomes essentially of finding a
representative sample (or subset) of the
population.
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 Note
that the population from which the
sample is drawn may not be the same as the
population about which we actually want
information.
 Sometimes they may be entirely separate for instance, we might study rats in order to
get a better understanding of human health,
or we might study records from people born
in 2008 in order to make predictions about
people born in 2009
 The population should be defined in
connection with the objectives of the study.
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
Define target population –the population for
which we generalize our research findings.

Target population could be much larger than the
study population.

It is critical to the success of the research project
to clearly define the target population-
Sampling frame: – the complete list of the
population units( in finite population case)
 Sampling units/study units: – the elements or
units considered for inclusion in the sample

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Sampling process

The sampling process comprises several stages:
• Defining the target population and study population
• Specifying a sampling frame - a set of people/ items
•
•
•
•
•
or events possible to measure
Specifying a sampling method for selecting items or
events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collection
Reviewing the sampling process
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Sampling


The process of obtaining a subset
(sample) of a larger group
(population) and use the results from
the sample to make decisions
/estimates about the population.
Faster and cheaper than investigating
the entire population(census)
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
Two keys
•
Selecting the right people/units
 Have to be selected scientifically so that
they are representative of the population
•
Selecting the right number of the right
people
 To minimize sampling errors,i.e. choosing
the wrong people by chance
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Characteristics of a good sample
 Representative
of the population
 Accessible
 Cost
effective
 Of the right size
 Obtained with minimum sampling error
 It should be suitable for analysis as per
the study design
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

The difference between an estimate
from an ideal sample and the true
population value is the sampling error.
Almost always, the sampling frame does
not match up perfectly with the target
population, leading to errors of
coverage.
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
Non-response is probably the most serious of these
errors.
• Arises in three ways:
 Inability of the person responding to come up
with the answer
 Refusal to answer
 Inability to contact the sampled elements
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 These
errors can be classified as due to
the interviewer, respondent, instrument,
or method of data collection.

Interviewers have a direct and dramatic effect on the
way a person responds to a question.
• Most people tend to side with the view apparently favored
by the interviewer, especially if they are neutral.
• Friendly interviewers are more successful.
• In general, interviewers of the same gender, racial, and
ethnic groups as those being interviewed are slightly more
successful.
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


Respondents differ greatly in motivation to answer
correctly and in ability to do so.
Obtaining an honest response to sensitive questions
is difficult.
Basic errors
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•
•
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Recall bias: simply does not remember
Prestige bias: exaggerates to ‘look’ better
Intentional deception: lying
Incorrect measurement: does not understand the units or
definition
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Types of samples/Sampling procedures
 Probability
sampling:
 Scientific approach to select representative
part of the population.
 Every possible sample has a probability of
selection which could be equal or
unequal,but predetermined .
 Inclusion probabilities of sampling units is
defined.
 Prejudiced selection/biased selection of
units is avoided
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

A probability sampling scheme is one in which
every unit in the population has a chance
(greater than zero) of being selected in the
sample, and this probability can be accurately
determined.
. When every element in the population does
have the same probability of selection, this is
known as an 'equal probability of selection' (EPS)
design. Such designs are also referred to as 'selfweighting' because all sampled units are given
the same weight.
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• simple random sampling
• systematic sampling
• stratified sampling
• cluster sampling
--Multistage and multi-phase
sampling ( not discussed )
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Simple Random Sampling(SRS)
 The
most elementary type of sampling,but
requires complete list ( knowledge about all
population units ).
 Units are independently and randomly
selected one at a time until the desired
sample size is achieved.
 If the unit is chosen only once without
replacing it in the population ,it is called
simple random sampling without
replacement(SRSWOR)
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A
simple procedure to select a simple
random sample is the lottery method. List all
the units and prepare slips containing the
serial numbers of the study units. Mix the
slips thoroughly ,select the slips one by one
and note down the numbers.
 The population units bearing the noted
numbers are the sample units selected.
 Continue the selection until we get the
required number of sample units.
 This procedure is difficult to use when the
population is large.
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 We
can use random number tables to obtain
random samples from a given population.
 The random number tables are composed
of the numbers 0 through 9 , with
approximately equal frequency.
 In every page digits are printed in blocks of
five rows and five columns.
 While selecting random numbers , we can
start with a random page, random row and
random column.
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 Now
softwares / random number
generators are available for getting the
list of random numbers.
 Random number generator
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 Advantages
 Estimates are easy to calculate.
 Simple random sampling is always
an
EPS design.
 Disadvantages
 If sampling frame is large, this method is
not practical.( Using software simplifies
the task!!! )
 Minority subgroups of interest in
population may not be present in sample
in sufficient numbers for study.
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 Systematic
sampling relies on
arranging the target population
according to some ordering scheme and
then selecting elements at regular
intervals through that ordered list.
 Systematic sampling involves a random
start and then proceeds with the
selection of every kth element from then
onwards. In this case, k=(population
size/sample size).
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 It
is important that the starting point is
not automatically the first in the list, but is
instead randomly chosen from within the
first to the kth element in the list.
 A simple example would be to select
every 10th name from the telephone
directory (an 'every 10th' sample, also
referred to as 'sampling with a skip of
10').
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 As
described above, systematic sampling is
an EPS method, because all elements have
the same probability of selection (in the
example given, one in ten). It is not 'simple
random sampling' because different subsets
of the same size have different selection
probabilities - e.g. the set {4,14,24,...,994}
has a one-in-ten probability of selection, but
the set {4,13,24,34,...} has zero probability of
selection.
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 ADVANTAGES:
 Sample is easy to select
 Suitable sampling frame
can be identified
easily
 Sample units are evenly spread over entire
reference population
 DISADVANTAGES:
 Sample may be biased if hidden periodicity
in population coincides with that of
selection.
 Difficult to assess precision of estimate from
one survey.
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Where population embraces a number of distinct
categories, the frame can be organized into separate
"strata." Each stratum is then sampled as an
independent sub-population, out of which individual
elements can be randomly selected.
 Every unit in a stratum has same chance of being
selected.
 Using same sampling fraction for all strata ensures
proportionate representation in the sample.
 Adequate representation of minority subgroups of
interest can be ensured by stratification & varying
sampling fraction between strata as required.
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Finally, since each stratum is treated as an
independent population, different sampling
approaches can be applied to different strata.
 Drawbacks :
 First, sampling frame of entire population has to
be prepared separately for each stratum
 Second, when examining multiple criteria,
stratifying variables may be related to some, but
not to others, further complicating the design,
and potentially reducing the utility of the strata.
 Finally, in some cases (such as designs with a
large number of strata, or those with a specified
minimum sample size per group), stratified
sampling can potentially require a larger sample
than would other methods

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Cluster sampling is an example of 'two-stage
sampling' .
 First stage- a sample of areas is chosen;
 Second stage- a sample of respondents within
those areas is selected.
 Population divided into clusters of
homogeneous units, usually based on
geographical contiguity.
 Sampling units are groups rather than
individuals.
 A sample of such clusters is then selected.
 All units from the selected clusters are studied.

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 Advantages
:
 Cuts down on the cost of preparing a
sampling frame.
 This can reduce travel and other
administrative costs.
 Disadvantages: sampling error is higher
for a simple random sample of same size.
 Often used to evaluate some health
schemes in epidemological studies.
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Types of cluster sampling methods.
One-stage sampling. All of the elements
within selected clusters are included in
the sample.
Two-stage sampling. A subset of
elements within selected clusters are
randomly selected for inclusion in the
sample
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 Although
strata and clusters are both nonoverlapping subsets of the population, they
differ in several ways.
 All strata are represented in the sample; but
only a subset of clusters are represented in
the sample.
 With stratified sampling, the best survey
results occur when elements within strata
are internally homogeneous. However, with
cluster sampling, the best results occur
when elements within clusters are internally
heterogeneous
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Self study topic
 Non-probability
sampling
 Explain
the different non-probability
sampling techniques
 When
 What
do you use these techniques?
are the drawbacks?
 Can
we use such sample data for detailed
statistical analysis?
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