Sampling is selecting observations (a sample) to provide an adequate description and inferences of the population. Sample: A is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005) The sampling frame A list of all elements or other units containing the elements in a population.

1. SAMPLING TECHNIQUES
Professor Dr. AB Rajar, MBBS, Dip-Diab, MPH, Ph.D. CPHE
Director of Research and Innovative Center
[IBN-E-SINA UNIVERSITY]
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Professor Dr AB Rajar

2. CONTENTS
 Sample definition
 Purpose of sampling.
 Stages in the selection of a sample.
 Issues in sampling…
 Types of sampling in quantitative research.
 Sampling errors….
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Professor Dr AB Rajar

3. SAMPLING…..
• Sampling is selecting
observations (a sample) to
provide an adequate
description and inferences
of the population.
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Professor Dr AB Rajar

4. POPULATION
• The larger group from
which individuals are
selected to participate in a
study
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Professor Dr AB Rajar

5. SAMPLE & SAMPLING FRAME.
• Sample:
• A is “a smaller (but hopefully
representative) collection of
units from a population used
to determine truths about
that population” (Field, 2005)
• The sampling frame
• A list of all elements or other
units containing the elements
in a population.
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Professor Dr AB Rajar

6. What exactly IS a “sample”?
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7. TARGET POPULATION……
• A set of elements larger
than or different from the
population sampled and to
which the researcher
would like to generalize
study findings.
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Professor Dr AB Rajar

8. SAMPLING
Population
Sample
Sampling
Frame
Sampling
Process
What you
want to
talk about
What you
actually
observe
in the
data
Inference
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10. THE PURPOSE OF SAMPLING….
• To gather data about the population to make an
inference that can be generalized to the population
• To identify participants from whom to seek some
information
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11. STAGES IN SELECTION OF THE SAMPLE
• Define the target population
• Select a sampling frame
• Determine if a probability or nonprobability sampling method will be chosen
• Plan procedure for selecting sampling unit
• Determine sample size
• Select actual sampling units
• Conduct fieldwork
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12. ISSUES IN SAMPLING…
• Nature of the sample (random samples).
• Size of the sample.
• Method of selecting the sample
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Professor Dr AB Rajar

13. ISSUES IN SAMPLING…
• Important issues:
• Representation:
• The extent to which the sample is representative of
the population.
• Generalization:
• The extent to which the results of the study can be reasonably
extended from the sample to the population.
• Sampling error:
• The chance occurrence that a randomly selected sample
is not representative of the population due to errors
inherent in the sampling technique
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Professor Dr AB Rajar

14. ISSUES IN SAMPLING…
• Important issues (continued):
• Sampling bias :
• Some aspect of the researcher’s sampling design
creates a bias in the data.
• Three fundamental steps:
• Identify a population.
• Define the sample size.
• Select the sample
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16. TYPES OF SAMPLES
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17. TYPES OF SAMPLES
• Probability (Random) Samples
• Simple random sample
• Systematic random sample
• Stratified random sample
• Cluster sample.
• Single, double, and Multistage, Multiphase sample
• Non-Probability Samples
• Convenience sample
• Purposive sample
• Quota
• Snowball sampling
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18. PROBABILITY SAMPLING
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19. PROBABILITY SAMPLING
• 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 'self-weighting' because all sampled
units are given the same weight.
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20. SIMPLE RANDOM SAMPLING
• Probability sampling procedure that gives every
element in the target population, and each possible
sample of a given size, an equal chance of being
selected.
• Selecting subjects so that all population members have
an equal and independent chance of being selected.
• Applicable: in small, homogeneous & readily available population
• Three techniques:
• Lottery method
• Table of random numbers
• Randomly generated numbers using a computer program
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21. • All subsets of the frame are given an equal probability.
• Random number generators.
SIMPLE RANDOM SAMPLING
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22. SIMPLE RANDOM SAMPLING.
• Selection process:
• Identify and define the target
population.
• Determine the desired sample size.
• List all members of the target
population.
• Assign all members on the list a
consecutive number.
• Select an arbitrary starting point
from a table of random numbers
and read the appropriate number
of digits
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23. Two simple steps of Simple Random Sample
• First step: define who are the sampling units
• Second step: draw up a sampling frame
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Professor Dr AB Rajar

24. Two simple steps of Simple Random Sample
• First step: define who are the sampling units, i.e.,
the people or items (e.g., households) who are to be
sampled.
• These units need to be defined clearly in terms of their
particular characteristics.
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25. Two simple steps of Simple Random Sample
• Second step: draw up a sampling frame, i.e., a list of all
the sampling units in the source population.
• The sampling frame should be comprehensive, complete, and
up-to-date, to minimize selection bias.
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26. Two simple steps of Simple Random Sample
• Once a suitable sampling frame has been identified, its
sampling units should be given a number. If 2000
individuals form the source population, each one should
be assigned a unique number between 1 and 2000.
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27. SAMPLE FRAME
• To draw a random sample from the source population,
we need a sampling frame, i.e., a complete
enumeration of the sampling units in the study
population.
• (A list of population elements from which units to be sampled
can be selected)
• The sampling unit may be an individual, household, or
school.
• Electoral registers may be a suitable sampling frame for
adults
• School attendance registers may be a suitable sampling frame
for children
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28. SIMPLE RANDOM SAMPLING
• ADVANTAGES:
1. Easy to conduct
2. High probability of achieving a representative
sample
3. Meets assumptions of many statistical
procedures.
• DISADVANTAGES.
1. Identification of all members of the population
can be difficult.
2. Contacting all members of the sample can be
difficult
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29. STRATIFIED RANDOM SAMPLING
• The population is divided into
two or more groups called
strata, according to some
criterion, such as:
• Geographic location,
• Grade level,
• Age, or income,
• and subsamples are randomly
selected from each stratum.
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30. STRATIFIED RANDOM SAMPLING
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31. STRATIFIED RANDOM SAMPLING (CONTINUED)
• ADVANTAGES:
• More accurate sample.
• Can be used for both proportional and non-proportional
samples.
• Representation of subgroups in the sample.
• DISADVANTAGES:
• Identification of all members of the population can be difficult.
• Identifying members of all subgroups can be difficult
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32.  The process of randomly selecting intact groups, not
individuals, within the defined population sharing
similar characteristics.
 Clusters are locations within which an intact group of
population members can be found.
Examples:
• Neighborhoods.
• School districts.
• Villages.
• Classrooms
CLUSTER SAMPLING
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33. • The population is
divided into subgroups
(clusters) like families.
• A simple random
sample is taken from
each cluster.
CLUSTER SAMPLING
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34. CLUSTER SAMPLING (CONTINUED)
• ADVANTAGES:
• Very useful when large populations are spread over a
large geographic region.
• Convenient and expedient.
• Do not need the names of everyone in the population
• DISADVANTAGES:
• Representation is likely to become an issue
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35. CLUSTER SAMPLING (CONTINUED)
• Selection process:
• Identify and define the population
• Determine the desired sample size
• Identify and define a logical cluster
• List all clusters that make up the population of
clusters
• Estimate the average number of population members
per cluster
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36. • Order all units in the sampling frame
• Then every nth number on the list is selected
• N= Sampling Interval
SYSTEMIC RANDOM SAMPLING
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37. • ADVANTAGES:
• Moderate cost; moderate usage.
• Simple to draw samples.
• Easy to verify
• DISADVANTAGES:
• Periodic ordering required
SYSTEMIC RANDOM SAMPLING
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38. NON-PROBABILITY SAMPLES
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39. • The probability of each case being selected from the
total population is not known.
• Units of the sample are chosen on the basis of personal
judgment or convenience.
• There are NO statistical techniques for measuring
random sampling error in a non-probability sample.
NON-PROBABILITY SAMPLES
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Professor Dr AB Rajar

40. • A. Convenience Sampling
• B. Quota Sampling
• C. Judgmental Sampling (Purposive Sampling).
• D. Snowball sampling
• E. Self-selection sampling
NON-PROBABILITY SAMPLES
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Professor Dr AB Rajar

41. NON-PROBABILITY SAMPLES
 A. CONVENIENCE SAMPLE
 Convenience sampling involves
choosing respondents at the
convenience of the researcher.
 Examples:
 Using family members or students in
a classroom
 Mall shoppers
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42. Advantages
• Very low cost.
• Extensively used/understood.
• Disadvantages
• Variability and bias cannot be
measured or controlled.
• Projecting data beyond the
sample is not justified.
• Restriction of Generalization
CONVENIENCE SAMPLE
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44. • B.QUOTA SAMPLING
• The population is first
segmented into mutually
exclusive sub-groups, just
as in stratified sampling
NON-PROBABILITY SAMPLES
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45. NON-PROBABILITY SAMPLES
• B. QUOTA SAMPLING
• Advantages
• Used when the research budget is
limited.
• Very extensively used/understood
• No need for a list of population
elements.
• Disadvantages
• Variability and bias cannot be
measured/controlled
• Time-Consuming.
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Professor Dr AB Rajar

46. NON-PROBABILITY SAMPLES
• C.JUDGEMENTAL SAMPLING
• Researcher employs his or her own "expert” judgment.
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Professor Dr AB Rajar

47. NON-PROBABILITY SAMPLES
• C.JUDGEMENTAL SAMPLING
• ADVANTAGES:
• There is an assurance of Quality response.
• Meet the specific objective.
• DISADVANTAGES:
• Bias selection of sample may occur.
• Time-consuming process.
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48. NON-PROBABILITY SAMPLES
• D.SNOWBALL SAMPLING
• The research starts with a key person and introduces
the next one to become a chain
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49. NON-PROBABILITY SAMPLES
• D.SNOWBALL SAMPLING
• ADVANTAGES:
• Low cost.
• Useful in specific circumstances & for locating rare populations
• DISADVANTAGES:
• Not independent.
• Projecting data beyond the sample is not justified
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50. NON-PROBABILITY SAMPLES
• E. SELF-SELECTION SAMPLING
• It occurs when you allow each case, usually individuals,
to identify their desire to participate in the research.
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51. NON-PROBABILITY SAMPLES
• E.SELF-SELECTION SAMPLING
• ADVANTAGES.
• More Accurate
• Useful in specific circumstances to serve the purpose
• DISADVANTAGES
• More costly due to Advertising.
• Mass is left
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Professor Dr AB Rajar

52. SAMPLING ERRORS…..!!!
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Professor Dr AB Rajar

53. • The errors which arise due to the use of sampling
surveys are known as the sampling errors.
• Two types of sampling errors:
• Biased Errors-
• Due to selection of sampling techniques; size of the
sample.
• Unbiased Errors / Random sampling errors:
• Differences between the members of the population
included or not included.
SAMPLING ERRORS
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Professor Dr AB Rajar

54. i. Specific problem selection.
ii. Systematic documentation of related research.
iii. Effective enumeration.
iv. Effective pre-testing.
v. Controlling methodological bias.
vi. Selection of appropriate sampling techniques.
METHODS OF REDUCING SAMPLING ERRORS
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55. Non-sampling errors refer to biases and mistakes in
selecting a sample.
 CAUSES FOR NON-SAMPLING ERRORS
Sampling operations
Inadequate response
Misunderstanding the concept.
Lack of knowledge.
Concealment of the truth.
Loaded questions
Processing errors
Sample size
NON-SAMPLING ERRORS
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56. THANK YOU
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Professor Dr AB Rajar