SAMPLING TECHNIQUES
Prof. Jobi Jacob
Vice Principal
KNN College of Nursing

KEYWORDS
 Population : It is a set of people with common
characteristics
 Target Population : The entire group of people to
which the Researcher wishes to generalize the
findings of the study
 Accessible Population : The available group from
which the Researcher selects the sample
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KEYWORDS
 Sample : A unit from which necessary data is
collected
 Sampling Frame : It is a physical representation of
objects or individuals important to the development
of the final study sample
 Sampling Design : The method used to select the
sample
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SAMPLING DESIGN PROCESS
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
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CLASSIFICATION OF SAMPLING TECHNIQUES
Sampling Techniques
Non Probability
Sampling Techniques
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
Probability
Sampling Techniques
• Convenience Sampling
• Quota Sampling
• Snowball Sampling
• Purposive Sampling
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PROBABILITY SAMPLING METHODS
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SIMPLE RANDOM SAMPLING
 Each element in the population has an equal
probability of selection
 Enumerate the list of elements on a sampling frame
 Slips of paper representing each element is folded
and kept in a bowl
 Pick out number of slips needed(samples)
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RANDOM NUMBERS
21 71 89 96
82 59 22 78
12 17 76 93
79 28 20 60
70 34 51 93
68 36 96 19
21 99 18 32
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STRATIFIED RANDOM SAMPLING
 A two-step process in which the population is
partitioned into subpopulations, or strata
 The strata should be mutually exclusive and
collectively exhaustive in that every population
element should be assigned to one and only one
stratum and no population elements should be
omitted
 The Elements within the stratum should be
homogeneous as possible
 Elements are selected from each stratum by a
random procedure
 A major objective of stratified sampling is to
increase precision without increasing cost
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CLUSTER RANDOM SAMPLING
 The target population is first divided into mutually
exclusive and collectively exhaustive subpopulations or
clusters
 Then a random sample of clusters is selected, based on
simple random sampling
 For each selected cluster, either all the elements are
included in the sample (one-stage) or a sample of
elements is drawn probabilistically (two-stage)
 Elements within a cluster should be as heterogeneous
as possible, but clusters themselves should be as
homogeneous as possible
 Ideally, each cluster should be a small-scale
representation of the population
 This is also called Multi-stage Sampling
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CLUSTER RANDOM SAMPLING
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SYSTEMATIC RANDOM SAMPLING
 Selecting every kth element of population
Step 1: Obtain a list of total population
Step 2 : Determine sample size
Step 3 : Find out k
Step 4 : A random number between 1 – k is selected. For ex. 23
Then the samples will be 23, 123, 223, 323, … till required
samples are needed.
needed
sample
total
population
total

k
Example 100
1000
100000


k
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NON-PROBABILITY SAMPLING METHODS
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CONVENIENCE SAMPLING
It attempts to obtain a sample of convenient elements
OR
it involves the samples which are readily available
Often, respondents are selected because they
happen to be in the right place at the right time.
Ex.
 Students in a class
 People at malls
 People on the street
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QUOTA SAMPLING
 It is similar to stratified random sampling
 Selecting the sample from each strata is by
convenient sampling method and not by simple
random sampling
 The strata is based on variables of study
Ex - Age, Gender, Educational qualification
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SNOWBALL SAMPLING
 Here an initial group of respondents is selected
 After being interviewed, these respondents are
asked to identify other samples who could belong to
the target population
 Subsequent respondents are selected based on the
referrals
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PURPOSIVE SAMPLING
 It is a form of convenience sampling in which the
samples are selected based on the judgement of
the researcher or whom the researcher feels is a
typical representative of the accessible population
Ex. – Cancer patients who have problem with port-
a-cath used for chemotherapy
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SAMPLING DESIGN CHOICE CONSIDERATIONS
Probability Non-probability
Cost More costly Less costly
Accuracy More accurate Less accurate
Time More time Less time
Acceptance Universal Reasonable acceptance
Generalisability Good Poor
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FACTORS AFFECTING SAMPLE SIZE
 Homogeneity of sampling units
 Confidence
 Precision
 Statistical power
 Analytic procedures
 Costs, Time & Personnel
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THANK YOU
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