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1. SAMPLING METHODS
SPEAKER:- Shubhanshu Gupta
TEACHER I/C: Dr. B L Verma
DATE:- 05/08/2014

2. What exactly is a “sample”?

3. What exactly is a “sample”?
A subset of the population, selected by
either “probability” or “non-
probability” methods. If you have a
“probability sample” you simply know
the likelihood of any member of the
population being included (not
necessarily that it is “random.”

4. TARGET POPULATION
STUDY POPULATION
SAMPLE

5. Target vs. accessible populations
• The target population is the population a
researcher would like to generalize to. Often
this isn’t possible, so the accessible population
is used. For example, a researcher might want
to target all male elementary teachers in the
India, but actually collects data from the male
elementary teachers in Delhi.

6. Need for sampling
1. Complete enumeration may not be possible.
2. Resources: Lower cost, Lesser demand on personnel.
3. Speed: Faster results due to lesser coverage.
4. Reliable information: Due to small size - better trained personnel,
more accurate methods, better supervision.
• To draw conclusions about population from sample, there are two
major requirements for a sample.
– Firstly, the sample size should be large.
– Secondly, the sample has to be selected appropriately
so that it is representative of the population. Sample
should have all the characteristics of the population.

7. Disadvantages of sampling
1. Sampling entails an argument from the fraction to the
whole. Validity depends on representativeness of the
sample.
2. Fails to provide precise information in case of small
segments containing few individuals.
3. Not necessary in studies where complete enumeration is
needed.
4. May cause a feeling of discrimination among the subjects
who are not included in the study.

8. Definitions
• Population: The target group to which the findings (of
a study) would ultimately apply is called population1
Or
• Population is the term statisticians use to describe a
large set or collection of items that have something in
common2.
• Sample: is that part of the target population which is
actually enquired upon or investigated1.
Or
• Sample is a subset of population, selected in such a way
that it is representative of the larger population2

9. Definitions cont..
• Sampling unit: is the unit of selection
• Unit of study or element: is the subject on which
information is obtained.
• Sampling frame: list of all sampling units in the target
population is called a sampling frame.
• Sample size: the number of units or subjects sampled
for inclusion in the study is called sample size.
• Sampling technique: Method of selecting sampling
units from sampling frame

10. Definitions (cont.)
Sampling: is the process of selecting a small
number of elements from a larger defined
target group of elements such that the
information gathered from the small group will
allow judgments to be made about the larger
groups.
conclusions based on the sample results may
be attributed only to the population sampled*.
.

11. SAMPLING PROCESS
Identifying and defining the target population
Describing the accessible population & ensuring
sampling frame
Specifying the sampling unit
Specifying sampling selection methods

12. Determining the sample size
Specifying the sampling plan
Selecting a desired sample

13. Assumptions of quantitative
sampling
We want to generalize to
the population.
Random events are
predictable.
Therefore…We can compare random
events to our results.
Probability sampling is
the best approach.

14. Assumptions of qualitative sampling
Social actors are not
predictable like objects.
Randomized events are
irrelevant to social life.
Probability sampling is
expensive and inefficient.
Therefore…
Non-probability
sampling is the best
approach.

15. Target population
Sampling frame
Sample
Population you want
to generalize results
to
Population you have
access to for your study
Study population
How can you get access to
study population?
Study actually done on?
1.
2.
3…..

16. Types of sampling in quantitative
researches
Probability
samples
Non-
probability
samples

17. Probability Techniques
Probability
techniques
Simple Random
Sampling
Systemic
Sampling
Stratified
Sampling
Disproportionate
Proportionate
Cluster
Sampling
Others

18. Non-probability Sampling
• Non probability sampling does not involve random selection
• Accidental or Purposive
Non-probability
techniques
Convenience
Sampling
Judgemental
Sampling
Quota Sampling
Snowball
Sampling

19. Conditions Favoring the Use of
Factors Nonprobability
sampling
Probability
sampling
Nature of research Exploratory Conclusive
Relative magnitude of sampling
and nonsampling errors
Nonsampling
errors are
larger
Sampling
errors are
larger
Variability in the population Homogeneous
(low)
Heterogeneous
(high)
Statistical considerations Unfavorable Favorable
Operational considerations Favorable Unfavorable

20. PROBABILITY
SAMPLING
TECHNIQUE

21. Features of the probability sampling
• It is a technique wherein the sample are gathered in a process that
given all the individuals in the population equal chances of being
selected.
• In this sampling technique, the researcher must guarantee that every
individual has an equal opportunity for selection.
• The advantage of using a random sample is the absence of both
systematic & sampling bias.
• The effect of this is a minimal or absent systematic bias, which is a
difference between the results from the sample & those from the
population.

22. Simple random sampling
• This is the most pure & basic probability sampling design.
• In this type of sampling design, every member of population has an
equal chance of being selected as subject.
• The entire process of sampling is done in a single step, with each
subject selected independently of the other members of the
population
• There is need of two essential prerequisites to implement the simple
random technique: population must be homogeneous & researcher
must have list of the elements/members of the accessible population.

23. SRS with replacement (SRSWR):
In SRSWR the units selected in the earlier draws are
replaced back in the population before the
subsequent draws are made. Thus a unit has a
chance of being included in the sample for more
than once.
SRS without replacement (SRSWOR) : Most common
In SRSWOR the units selected in the earlier draws
aren’t replaced back in the population before the
subsequent draws are made. Thus a unit has only
one chance of being included in the sample.

24. The first step of the simple random sampling technique is to
identify the accessible population & prepare a list of all the
elements/members of the population. The list of the subjects in
population is called as sampling frame & sample drawn from
sampling frame by using following methods:
The lottery method
The use of table of random numbers
The use of computer

25. The lottery method…
It is most primitive & mechanical method.
Each member of the population is assigned a unique number.
Each number is placed in a bowl or hat & mixed thoroughly.
The blind-folded researcher then picks numbered tags from the
hat.
All the individuals bearing the numbers picked by the researcher
are the subjects for the study.

26. Lottery method
Lottery method

27. Different random number table:
• Tipetts (1927) Random Number Table
• Fisher & Yates (1938)
• Kendall & Babington Smith`s (1939)
• Rand Corporation (1955) table of random numbers.
• C.R-Rao, Mitra & Mathai (1966) table of random numbers

28. Random number table
76 58 30 83 64
47 56 91 29 34
10 80 21 38 84
00 95 01 31 76
07 28 37 07 61

29. The use of table of random numbers…
This is most commonly & accurately used method in simple
random sampling.
Random table present several numbers in rows & columns.
Researcher initially prepare a numbered list of the members of
the population, & then with a blindfold chooses a number from
the random table.
The same procedure is continued until the desired number of the
subject is achieved.
If repeatedly similar numbers are encountered, they are ignored
& next numbers are considered until desired numbers of the
subject are achieved.

30. The use of computer…
Nowadays random tables may be generated from the
computer , & subjects may be selected as described in
the use of random table.
For populations with a small number of members, it is
advisable to use the first method, but if the population
has many members, a computer-aided random selection
is preferred.
Excel:
enter the function =RND() on any blank cell

31. Merits and Demerits
Merits
• Ease of assembling the
sample
• Fair way of selecting a
sample
• Require minimum
knowledge about the
population in advance
• It unbiased probability
method
• Free from sampling errors
Demerits
• It requirement of a complete &
up-to-date list of all the
members of the population.
• Does not make use of
knowledge about a population
which researchers may already
have.
• Lots of procedure need to be
done before sampling
• Expensive & time-consuming

32. Systematic Random 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
• Systematic sampling can be applied so that an essentially random
sample is drawn.

33. 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).
Number of subjects in target population (N)
K = N/n or
K= 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 5th person will be selected.

34. If the ordering of the elements produces a cyclical
pattern, systematic sampling may decrease the
representativeness of the sample.
For example, there are 100,000 elements in the
population and a sample of 1,000 is desired. In this
case the sampling interval, i, is 100. A random
number between 1 and 100 is selected. If, for
example, this number is 23, the sample consists of
elements 23, 123, 223, 323, 423, 523, and so on.

35. Systematic random sampling(contd.)
• For example, if there are 100 patients (N) in a hospital
and to select a sample of 20 patients (n) by systematic
random sampling procedure,
• Step 1: write the names of 100 patients in alphabetical
order or their roll numbers one below the other.
• Step 2: sampling fraction: divide N by n to get the
sampling fraction (k).In the example k=100/20 = 5.
• Step 3: randomly select any number between 1 to k i.e.
between 1 to 5. Suppose the number we select is 4.
• Step 4: patient number 4 is selected in the sample.
• Step 5: Thereafter every 4+k th patient is selected in the
sample until we reach the last one.

36. Systematic random sampling(contd.)

37. Merits and Demerits
Merits
• Convenient & simple to carry
out.
• Distribution of sample is
spread evenly over the entire
given population.
• Less cumbersome, time-
consuming, & cheaper
Demerits
• If first subject is not randomly
selected, then it becomes a
nonrandom sampling
technique
• Sometimes this may result in
biased sample.
• If sampling frame has
nonrandomly, this sampling
technique may not be
appropriate to select a
representative sample.

38. Systematic random sampling(contd.)
• Example:
–District level household & facility survey
for Reproductive and child health – 1998-
99, 2002-03, 2010-11.
–Random blinded rechecking of slides under
RNTCP. Slides are drawn from the register
by systematic random sampling.

39. Stratified Random Sampling
• This method is used for heterogeneous population.
• It is a probability sampling technique wherein the
researcher divides the entire population into different
homogeneous subgroups or strata, & then randomly selects
the final subjects proportionally from the different strata.
• The strata are divided according selected traits of the
population such as age, gender, religion, socio-economic
status, diagnosis, education, geographical region, type of
institution, type of care, type of registered nurses, nursing
area specialization, site of care, etc.

40. Stratified Sampling
In proportionate stratified sampling, the size of
the sample drawn from each stratum is
proportionate to the relative size of that stratum in
the total population.
In disproportionate stratified sampling, the size
of the sample from each stratum is not
proportionate to the relative size of that stratum
and to the standard deviation of the distribution of
the characteristic of interest among all the
elements in that stratum.

41. Stratified random sampling(contd.)
• For example, if we draw a simple random sample from a
population, a sample of 100 may contain
– 10 to 15 from high socioeconomic group
– 20 to 25 from middle socioeconomic group
– 70 to 75 from low socioeconomic group
• To get adequately large representation for all the three socio
economic structures, we can stratify on socioeconomic class
and select simple random samples from each of the three
strata.

42. POPULATION
LOW
SOCIOECONOMIC
MIDDLE
SOCIOECONOMIC
HIGH
SOCIOECONOMIC

44. Merits and Demerits
Merits
• It representation of all group in
a population
• For observing relation between
subgroup
•
• Observe smallest & most
inaccessible subgroups in
population
• Higher statistical precision
• Save lot of time, money, &
effort
Demerits
• It require accurate information
on the proportion of population
in each stratum.
• Large population must
available from which select
sample
• Possibility of faulty
classification

45. Cluster sampling
• The population is divided into subgroups (clusters) like
families. A simple random sample is taken of the subgroups
and then all members of the cluster selected are surveyed.
• Cluster sampling is used when the population is
heterogeneous.
• Clusters are formed by grouping units on the basis of their
geographical locations.
• Cluster sampling is a very useful method for the field
epidemiological research and for health administrators.

46. Cluster sampling

47. Cluster sampling
Cluster 4
Cluster 5
Cluster 3
Cluster 2Cluster 1

48. Cluster sampling (contd.)
• A special form of cluster sampling called the “30 X 7
cluster sampling”, has been recommended by the WHO
for field studies in assessing vaccination coverage.
• In this a list of all villages (clusters) for a given
geographical area is made.
• 30 clusters are selected using Probability Proportional
to Size (PPS).
• From each of the selected clusters, 7 subjects are
randomly chosen.
• Thus a total sample of 30 x 7 = 210 subjects is chosen.
• The advantage of cluster sampling is that sampling
frame is not required

49. CLUSTER SAMPLING
It is sometimes called
area sampling because
this is usually applied
when the population is
large. In this
technique, groups or
clusters instead of
individuals are
randomly chosen.

50. • 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).

51. Merits and Demerits
Merits
• It cheap, quick, & easy for
a large population.
• Large population can be
studied, & require only
list of the members.
• Investigators to use
existing division such as
district, village/town, etc.
• Same sample can be used
again for study
Demerits
• This technique is the least
representative of the
population.
• Possibility of high
sampling error
• This technique is not at all
useful.

52. MULTI-STAGE SAMPLING
 Refers to a sampling techniques which is carried out in
various stages.
 Population is regarded as made of a number of primary units
each of which further composed of a number of secondary
units.
 Consists of sampling first stage units by some suitable
method of sampling.
 From among the selected first stage units, a sub- sample of
secondary stage units is drawn by some suitable method of
sampling which may be same as or different from the method
used in selecting first stage unit.

53. Multistage random sampling
• In this method, the whole population is divided in first stage
sampling units from which a random sample is selected.
• The selected first stage is then subdivided into second stage units
from which another sample is selected.
• Third and fourth stage sampling is done in the same manner if
necessary.
• Example:
– NFHS data is collected by multistage sampling.
• Rural areas – 2 stage sampling – Villages from list by PPS,
Households from village
• Urban areas – Wards (PPS) – CEB (PPS) – 30 households
from each CEB

54. • Combination of all the methods described above.
Involves selecting a sample in at least two stages.
e.g: i. Stage 1: Stratified Sampling
Stage 2: Systematic Sampling
e.g: ii. Stage 1: Cluster Sampling
Stage 2: Stratified Sampling
Stage 3: Simple Random Sampling
Multi-stage Sampling

55. Stratified Cluster sampling

56. Advantages:
II stage units are necessary only for selected I stage units
Flexible & allows different selection procedure
Easier to administer
A large number of units can be sampled for a given cost.
.

57. Sequential Sampling
• This method of sample selection is slightly
different from other methods.
• Here the sample size is not fixed. The
investigator initially selects small sample &
tries out to make inferences; if not able to draw
results, he or she then adds more subjects until
clear-cut inferences can be drawn.

58. Merits and Demerits

59. NONPROBABILITY
SAMPLING
TECHNIQUE

60. Features of the nonprobability
sampling
• It is a technique wherein samples are gathered in a process
that does not give all the individual in the population equal
chances of being selected.
• Most researchers are bound by time, money, & workforce,
& because of these limitations, it is almost impossible to
randomly sample the entire population & it is often
necessary to employ another sampling technique, the
nonprobability sampling technique.
• Subject in a nonprobability sample are usually selected on
the basis of their accessibility or by the purposive personal
judgment of the researcher

61. Uses of Non probability Sampling
• This type of sampling can be used when demonstrating that a
particular trait exists in the population.
• It can also be used when researcher aims to do a qualitative, pilot ,
or exploratory study.
• It can be used when randomization is not possible like when the
population is almost limitless.
• it can be used when the research does not aim to generate results
that will be used to create generalizations.
• It is also useful when the researcher has limited budget, time, &
workforce.
• This technique can also be used in an initial study (pilot study)

62. Types of the Nonprobability
Sampling
Purposive/judgmental sampling
Convenience/grab/availability sampling
Consecutive sampling
Quota sampling
Snow ball sampling

63. Purposive/deliberate sampling
• It is more commonly known as ‘judgmental’ or ‘authoritative sampling’.
• In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind.
• In purposive sampling, the researcher believes that some subjects are fit for research compared to
other individual. This is the reason why they are purposively chosen as subject.
• In this sampling technique, samples are chosen by choice not by chance, through a judgment made
the researcher based on his or her knowledge about the population
• It is more commonly known as ‘judgmental’ or ‘authoritative sampling’.
• In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind.
• In purposive sampling, the researcher believes that some subjects are fit for research compared to
other individual. This is the reason why they are purposively chosen as subject.
• In this sampling technique, samples are chosen by choice not by chance, through a judgment made
the researcher based on his or her knowledge about the population

64. For example, a researcher wants to study the lived experiences of
post disaster depression among people living in earthquake
affected areas of Gujarat.
In this case, a purposive sampling technique is used to select the
subjects who were the victims of the earthquake disaster & have
suffered post disaster depression living in earthquake-affected
areas of Gujarat.
In this study, the researcher selected only those people who
fulfill the criteria as well as particular subjects that are the typical
& representative part of population as per the knowledge of the
researcher.

65. Merits and Demerits
Merits
• Simple to draw sample & useful
in explorative studies
• Save resources, require less
fieldwork.
Demerits
• Require considerable knowledge
about the population under study.
• It is not always reliable sample, as
conscious biases may exist.
• Two main weakness of purposive
sampling are with the authority &
in the sampling process.
• It is usually biased since no
randomization was used to
obtained the sample.

66. Convenience/haphazard Sampling
• It is probably the most common of all sampling techniques because
it is fast, inexpensive, easy, & the subject are readily available.
• It is a nonprobability sampling technique where subjects are selected
because of their convenient accessibility & proximity to the
researcher.
• The subjects are selected just because they are easiest to recruit for
the study & the researcher did not consider selecting subjects that
are representative of the entire population
• It is also known as an accidental sampling.
• Subjects are chosen simply because they are easy to recruit.

67. the process of including whoever happens to
be available at the time
…called “accidental” or “haphazard”
sampling

68. For example, if a researcher wants to conduct a study on
the older people residing in Jhansi, & the researcher
observes that he can meet several older people coming
for morning walk in a park located near his residence in
Jhansi, he can choose these people as his research
subjects.
These subjects are readily accessible for the researcher
& may help him to save time, money, & resources.(man
on the streets)

69. Merits and Demerits
Merits
• This technique is considered
easiest, cheapest, & least
time consuming.
• This sampling technique
may help in saving time,
money, & resources.
Demerits
• Sampling bias, & the sample
is not representative of the
entire population.
• It does not provide the
representative sample from
the population of the study.
• Findings generated from
these sampling cannot be
generalized on the
population.

70. Consecutive Sampling
• It is very similar to convenience sampling except that it
seeks to include all accessible subjects as part of the sample.
• This nonprobability sampling technique can be considered
as the best of all nonprobability samples because it include
all the subjects that are available, which makes the sample a
better representation of the entire population.
• It is also known as total enumerative sampling.

71. In this sampling technique, the investigator pick up all
the available subjects who are meeting the preset
inclusion & exclusion criteria.
This technique is generally used in small-sized
populations.
For example, if a researcher wants to study the activity
pattern of post kidney-transplant patient, he can selects
all the post kidney transplant patients who meet the
designed inclusion & exclusion criteria, & who are
admitted in post-transplant ward during a specific time
period.

72. Merits and Demerits
Merits
• Little effort for sampling
• It is not expensive, not time
consuming, & not
workforce intensive.
• Ensures more
representativeness of the
selected sample.
Demerits
• Researcher has not set plans
about the sample size &
sampling schedule.
• It always does not guarantee
the selection of
representative sample.
• Results from this sampling
technique cannot be used to
create conclusions &
interpretations pertaining to
the entire population.

73. Quota Sampling
• It is nonprobability sampling technique wherein the
researcher ensures equal or proportionate representation of
subjects, depending on which trait is considered as the basis
of the quota.
• The bases of the quota are usually age, gender, education,
race, religion, & socio-economic status.
• For example, if the basis of the quota is college level & the
research needs equal representation, with a sample size of
100, he must select 25 first-year students, another 25
second-year students, 25 third-year, & 25 fourth-year
students.

74. Types of Quota Sampling
• Proportional quota sampling – representing the major
characteristics of the population by sampling a proportional amount
of each.
• Non-proportional quota sampling is a bit less restrictive. In
this method, you specify the minimum number of sampled units you
want in each category. Here, you're not concerned with having
numbers that match the proportions in the population. Instead, you
simply want to have enough to assure that you will be able to talk
about even small groups in the population.

75. Quota sampling may be viewed as two-stage restricted
judgmental sampling.
– The first stage consists of developing control categories, or
quotas, of population elements. – to develop these quotas
the researcher lists relevant control characteristics and
determine the distribution of these characteristics in the
target population. The relevant control characteristics
(gender, age, race) are identified on the basis of judgment
– In the second stage, sample elements are selected based on
convenience or judgment. Once the quota have been
assigned, there is considerable freedom of selecting the
element to be included in the sample. The only requirement
is that the element selected fit the control characteristics.

76. Merits and Demerits
Merits
• Economically cheap, as
there is no need to approach
all the candidates.
• Suitable for studies where
the fieldwork has to be
carried out, like studies
related to market & public
opinion polls.
Demerits
• It not represent all
population
• In the process of sampling
these subgroups, other
traits in the sample may be
overrepresented.
• Not possible to estimate
errors.
• Bias is possible, as
investigator/interviewer can
select persons known to
him.

77. Snowball/networking Sampling
• It is a nonprobability sampling technique that is used by
researchers to identify potential subjects in studies where
subjects are hard to locate such as commercial sex workers,
drug abusers, etc.
• For example, a researcher wants to conduct a study on the
prevalence of HIV/AIDS among commercial sex workers.
• In this situation, snowball sampling is the best choice for
such studies to select a sample.
• This type of sampling technique works like chain referral.
Therefore it is also known as chain referral sampling.

78. After observing the initial subject, the researcher asks
for assistance from the subject to help in identify people
with a similar trait of interest
The process of snowball sampling is much like asking
subjects to nominate another person with the same trait.
The researcher then observes the nominated subjects &
continues in the same way until obtaining sufficient
number of subjects.

80. Merits and Demerits
Merits
• The chain referral process
allows the researcher to
reach populations that are
difficult to sample when
using other sampling
methods.
• The process is cheap,
simple, & cost-efficient.
• Need little planning & lesser
workforce
Demerits
• Researcher has little control
over the sampling method.
• Representativeness of the
sample is not guaranteed.
• Sampling bias is also a fear
of researchers when using
this sampling technique.

81. Errors in Sampling
• Non-Observation Errors
– Sampling error: naturally occurs
– Coverage error: people sampled do not match the
population of interest
– Underrepresentation
– Non-response: won’t or can’t participate

82. Errors of Observation
• Interview error- interaction between interviewer and person
being surveyed
• Respondent error: respondents have difficult time answering
the question
• Measurement error: inaccurate responses when person doesn’t
understand question or poorly worded question
• Errors in data collection