The document discusses the principles of sampling in nursing research, outlining definitions, methods, and importance of both quantitative and qualitative sampling techniques. It highlights various sampling methods such as simple random, stratified, systematic, cluster, and multi-stage sampling, along with their advantages and disadvantages. Additionally, it addresses challenges in sampling, including sampling errors and biases, and emphasizes the need for proper sampling to ensure research accuracy.

1. M.Sc POST GRADUATE DEGREE IN NURSING
Nursing Research and Statistics
Unit -VI
SAMPLING
Sampling techniques
Prof Mrs.G. Sivagami

2. Objectives
Define sampling
Explain population
Understand methods of quantitative sampling
Discuss methods of qualitative sampling
List down the problems of sampling
Discuss on sampling error

3. INTRODUCTION
Sampling is a process of selecting representative units
from an entire population of a study.
It is not always possible to study an entire population;
therefore, the researcher draws a representative part
of a population through sampling process.
In other words, sampling is the selection of some part
of an aggregate or a whole on the basis of which
judgments or inferences about the aggregate or mass
is made.

4. Population is defined as the entire mass of
observation, which is the parent group from which
a sample is to be formed.
Sample is defined as the aggregate of objects,
person or elements, selected from the universe.

5. Sampling- The method of taking the
sample is known as sampling.

6. Importance
of
Sampling
Economical
Accuracy
Save
time
and
efforts
Easily
approachable
Errors can
be
controlled
Practical

7.  Important Terminologies
◦ 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 of occurrence that a randomly selected
sample is not representative of the population due to errors
inherent in the sampling technique

8.  Important terminologies (continued)
◦ Sampling bias
 It is a bias in which a sample is collected in such a way
that some members of the intended population have a
lower sampling probability than others.
◦ Three fundamental steps in quantitative sampling
 Identify a population
 Define the sample size
 Select the sample

9. Sampling Techniques
Probability
Non
Probability

10. Probability sampling
• Every unit of the
population has an equal
chance of being
selected for the sample.
Non probability
sampling
• Sampling techniques
one cannot estimate
beforehand the chance
of each element being
included in the sample.

11. A. Simple random sampling
B. Stratified random sampling
C. Systematic sampling
D. Cluster sampling
E. Multi-stage sampling
Probability sampling

12. A. Simple Random sampling is applied when the
method of selection assures each individual element in
the universe an equal chance of being chosen.

13. Random numbers of table
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1
4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0
2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3
2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6

14. Simple random
sampling

15. Types of Simple Random Sample
With Replacement
The unit once selected has the chance for
again selection
Without Replacement
The unit once selected can not be
selected again

16. Lottery Method
Tippet’s Number
Grid method

17. Method of The Lottery
• Lottery is one of the oldest methods and is a mechanical
example of random sampling.
• In this method, each member of the population has to be
numbered systematically and in a consequent manner by
writing each number on a separate piece of paper.
• These pieces of paper are mixed and put into a box and
then numbers are drawn out of the box in a random
manner.

18. Advantages
Conjunction
with other
methods
unbiased
Easy to find
errors
Equal
chance

19. Consumes
time and
energy
More
chances of
misleading
sample
Difficult for
compariso
n study

20. B. Stratified sampling - When the
population is divided into different strata
then samples are selected from each
stratum by simple random sampling or
by regular interval method we call it as
stratified random sampling method.

22. Advantages
• Comparing sub-
categories
• Saves time, money
and energy
• Can represent
various group

23. Requires more
efforts
Needs a larger sample
size
Strata are overlapping, chances of bias

24. Stratified
sampling
Disproportionate
sampling
Proportionate
sampling

25. Disproportionate stratification
Disproportionate stratification is a type of stratified
sampling. With disproportionate stratification, the sample
size of each stratum does not have to be proportionate to
the population size of the stratum. This means that two or
more strata will have different sampling fractions.

26. For example,
In disproportionate stratified random sampling, the different
strata do not have the same sampling fractions as each
other. For instance, if your four strata contain 200, 400,
600, and 800 people, you may choose to have different
sampling fractions for each stratum. Perhaps the first
stratum with 200 people has a sampling fraction of ½,
resulting in 100 people selected for the sample, while the
last stratum with 800 people has a sampling fraction of ¼,
resulting in 200 people selected for the sample

27. Proportionate Stratified Random Sample
In proportional stratified random sampling, the size of each
stratum is proportionate to the population size of the strata
when examined across the entire population. This means
that each stratum has the same sampling fraction.

28. For example, let’s say you have four strata with population
sizes of 200, 400, 600, and 800. If you choose a sampling
fraction of ½, this means you must randomly sample 100,
200, 300, and 400 subjects from each stratum
respectively. The same sampling fraction is used for each
stratum regardless of the differences in population size of
the strata.

29. C. Systemic sampling
This sampling is obtaining a collection of elements
by drawing every nth
person after that; n is a number
termed as sampling interval.

30. C. Systemic sampling
It can be likened to an arithmetic progression,
wherein the difference between any two consecutive
numbers is the same.
It involves the selection of every Kth case from list of
group, such as every 10th person on a patient list or
every 100th person from a phone directory.
Systematic sampling is sometimes used to sample
every Kth person entering a bookstore, or passing down
the street or leaving a hospital & so forth.

31. C. Systemic sampling
If we had a list of subjects or sampling frame, the
following procedure could be adopted. The desired
sample size is established at some number (n) &
the size of population must know or estimated (N).
K = N/n
Size of sample
For example, a researcher wants to choose about
100 subjects from a total target population of 500
people. Therefore, 500/100=5. Therefore, every
5thperson will be selected.

32. Advantages
•Easy to use
Disadvantages
•Over representation of several
groups is greater.

33. D. Cluster Sampling-
The whole population is surveyed and such areas are
located wherein elements are seen clustering
themselves and sample is selected from such clusters
and they reflect all characteristics of the Universe.

35. Section 4
Section 5
Section 3
Cluster sampling
Section 2
Section 1

36. Advantage
• Low cost/high frequency of use
• Requires list of all clusters, but only of individuals
within chosen clusters
• Can estimate characteristics of both
cluster and population
• For multistage, has strengths of used methods
• Researchers lack a good sampling
frame for a dispersed population
• Easier to apply larger Geographical area

37. Disadvantage
s
Not good representative of the
population
Sampling error
Same individual can belong to
two clusters and studied twice

38. D. Multi stage sampling
Sample is selected in various stages but only
last sample is studied.

39. Multistage sampling refers to sampling plans where
the sampling is carried out in stages using smaller
and smaller sampling units at each stage.
Not all Secondary Units Sampled normally used to
overcome problems associated with a geographically
dispersed population
Multistage Random Sampling

40. 1
2
3
4
5
6
7
8
9
10
Primary
Clusters
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Secondary
Clusters Simple Random Sampling within Secondary Clusters

41. Advantages
•Good representative of population
•Improvement of other sampling
methods
Disadvantages
•Difficult and complex method

42. A. Incidental/ Accidental sampling
B. Convenience sampling
C. Purposive sampling
D.Quota sampling
Non Probability Sampling

43. A. Incidental or Accidental sampling
means selecting the units on basis of
easy approaches.

44. Advantages
• Easy and quick results
• Saves time, money and
energy
Disadvantages
• Not representative of
population
• Cannot produce reliable
results

45. B. Convenience method, the
investigator selects certain items are to
his convenience. No pre planning is
necessary for the selection of items.

46. disadvantages
• Biased data
• Not
representative
population
Advantages
• Easy method
• Economical

47. C. Purposive sampling- The selection of
elements is based upon the judgement of the
researcher, the purposive sampling is called
judgement sample

48. Advantage
• Control on variable
Disadvantage
•Reliability of criterion
is questionable

49. D. Quota sampling:-In the quota sampling the
interviewers are instructed to interview a
specified number of persons from each
category.

50. • Practical
• Economical
Advantages
• Not true
representative
• Not free from
error
disadvantages

51. Technique Strength Weakness
Probability
Simple Random
Sampling
Easily understood, results
projectable
Expensive, assurance of
representative
Stratified sampling Includes all important sub
populations
Expensive, Difficult to
select relevant
stratification variables
Systemic sampling Increases
representativenes
Can decrease
representative
ness
Cluster sampling Easy to implement, cost
effective
Difficult to interpret
results
Non probability
Convenience sampling Least expensive, least
time consuming.
Quota sampling Sample can be controlled
for certain characteristics
Bias, no assurance
of
representativeness

52. Choosing non Probability vs. Probability sampling
Conditions favouring the use of
Factors Non probability
sampling
Probability sampling
Nature of research Exploratory Conclusive
Relative magnitude
of sampling and
non sampling
errors
Non sampling errors
are larger
Sampling errors are
larger
Variability in
the population
Homogeneous Heterogeneous
Statistical
consideration
Unfavourable Favourable
Operational
considerations
Favourable Unfavourable

53. SAMPLING IN
QUALITATIVE
RESEARCH

54. Researchers in qualitative research select
their participants according to their :
1) Characteristics
2) Knowledge

55. The purposeful sampling
It is when the researcher chooses persons or
sites which provide specific knowledge about the
topic of the study.

56. 1) Maximal Variation
Sampling
2) Typical Sampling
3) Theory or Concept
Sampling
4) Homogeneous Sampling
5) Critical Sampling
6) Opportunistic Sampling
7) Snowball Sampling

57. It is when you select individuals that differ on a
certain characteristic. In this strategy you should first
identify the characteristic and then find individuals or
sites which display that characteristic.
1. Maximal Variation Sampling

58. It is when you study a person or a site that is “typical” to
those unfamiliar with the situation. You can select a typical
sample by collecting demographic data or survey data
about all cases.
2. Typical Sampling

59. It is when you select individuals or sites because they
can help you to generate a theory or specific concepts
within the theory. In this strategy you need a full
understanding of the concept or the theory expected to
discover during the study.
3. Theory or Concept Sampling

60. 4. Homogeneous Sampling
It is when you select certain sites or people because they
possess similar characteristics. In this strategy, you need
to identify the characteristics and find individuals or sites
that possess it.

61. It is when you study an exceptional case
represents the central phenomenon in
dramatic terms.
5. Critical Sampling

62. It is used after data collection begins, when you may find
that you need to collect new information to answer your
research questions.
6. Opportunistic Sampling

63. Snowball Sampling
It is when you don't know the best people to study
because of the unfamiliarity of the topic or the
complexity of events. So you ask participants during
interviews to suggest other individuals to be sampled.

64. PROBLEMS OF SAMPLING
Sampling errors
Lack of sample representativeness
Difficulty in estimation of sample size
Lack of knowledge about the sampling process
Lack of resources
Lack of cooperation
Lack of existing appropriate sampling frames for larger
population

65. Errors
Sampling
Errors
Biased
errors
Unbiased
errors
Non Sampling
Errors

66. SAMPLING ERROR
It is the deviation of the selected sample from the
true characteristics, traits, behaviours, qualities or
figures of the entire population.

67. CAUSES OF SAMPLING ERROR
Individual difference of subjects: Researchers
draw different subjects from the same population but
still the subjects have individual difference. A sample
is only, a subset of the entire population. Therefore,
there may be a difference between the sample and the
population.
Biased sampling procedure:It is a tendency to
favour a selection of sample units that posses a
particular characteristics. It may occur in the form of
over and under-representation bias.

68. Chance error :
The error occurs by chance. e.g. – Someone did a
comparative study on mal-nutrition on under five children in
two cities A and B. Unfortunately if city B had a large
number of slum dwellers, so, this will comprise a large
number of mal-nourished children than city-A which is going
to give a skewed result.
A process of randomization and probability sampling is
done to minimize such kind of sampling errors but it is still
possible that all the randomized subjects are not the
representative of the population
CONT…

69. Difficulty in estimation of sample size:
There is lack of availability of population parameters; in the
absence of population parameters it becomes very difficult
to estimate the sample size. Studies conducted on
undetermined sample size do not facilitate to generalize
the findings for the particular population.
Lack of knowledge about the sampling
process
If the researcher is having superficial knowledge about the
sampling methods and sampling process then this will lead
to poor selection and ultimately affects the study results

70. Lack of re-sources
Resource allocation for nursing research studies are very
poor in developing country like India. So in the absence of
appropriate resources drawing a good representative
sample is very difficult and biased too
Lack of cooperation
Sometimes study subjects are not co-operative with
researcher and after selection of samples by means of
simple random sampling they are not willing to participate
in the study, at this time, then it will create a problem for the
researcher in sampling.

71. OVER-COMING STRATEGIES
There is only one way to eliminate this kind of error...that ,
to eliminate the concepts of sample and to test the entire
population, which is really not possible, so, one has to
adapt then... a following strategies for overcoming these
types of problems...these are –
Use of proper and un-biased probability sampling
Avoid convenient or judgmental sampling.
Ensure that the target population is well defined and the
sample frame should match it as much as possible
.
Increasing the size of sample.
Inclusion and exclusion criteria

72. Increasing the size of sample :
SAMPLING
ERROR
SAMPLING
ERROR
sampl
e
sampl
e

73. NON SAMPLING ERRORS
A non-sampling error is a statistical term that
refers to an error that results during data
collection, causing the data to differ from the true
values. A non-sampling error differs from a
sampling error. In general, the sampling errors
decrease as the sample size increases, whereas
non-sampling error increases as the sample size
increases.

74. CAUSES
i) Lack of trained and qualified investigators
ii) Due to wrong answers to the questions
iii) Due to incomplete coverage
iv) Biasness of the investigators
v) Vague questionnaire
vi) Faulty list of population
vii) Wrong method of asking questions
viii) Wrong calculations while processing the data
ix) Failure of respondent memory to recall past
events
x)Error while printing the results

75. SUMMARY
Sampling enables the selection of right data
points from within the larger data set to estimate
the characteristics of the whole population.
Sampling helps a lot in research. It is one of the
most important factors which determines the
accuracy of your research/survey result. If
anything goes wrong with your sample then it will
be directly reflected in the final result.

76.  Creswell, J., W. (2012) Educational research: Planning,
Conducting, and Evaluating Quantitative and Qualitative
Research, 4th ed.
 Patton, M.Q. (2002). Qualitative Research and
Evaluation Methods. Thousand Oaks, CA: Sage.