This Presentation Will lead you towards a deep and neat study of the research sample and survey. It will be based on the main concepts of sampling types of sampling, types of surveys. Types of Sampling : Probability and Non-probability Probability sampling methods: Simple random sampling Cluster sampling Systematic Sampling Stratified Random sampling 2. Non-Probability: Convenience sampling Consecutive sampling Quota sampling Judgmental or Purposive sampling Snowball sampling.

1. CHAPTER 7
Sampling
By Niranjan H N

2. Definition BY Good and Hatt
• “A sample as the name implies, is a smaller
representation of a larger whole.”

3. Survey Can Be Conducted By Either
Of Two Methods
1. Census Method or
Parametric method and
2. Sampling method or
Non-parametric method.

4. Population
• Population or universe means, the entire mass of
observations, which is the parent group from which a
sample is to be formed.
• In census survey, the count of individuals (men, women
and children) is known as population.
• Research methodology population means characteristics
of a specific group.
• For example, secondary school teachers of, who
have some specific features like teaching
experience, teaching attitudes etc.

5. Target Population
One type of population distinguished by educational
researchers is called the target population.
By target population, also called universe, we mean all
the members of a real or hypothetical set of people ,
events or objects to which we wish to generalize the
results of our research.
The first step in sampling is to define the
target population.

6. Assumptions
of sampling
Homogeneity amidst
complexity
Possibility of
Representative Selection
Absolute accuracy not
essential

7. Need Of Sampling
Economy Of Time
Economy Of
Money
Collects Richer
Data
Utility In Experimental
Study
It Has Reliability Because It Is Based On
Probability Theory.

8. Types of Sampling
Probability sampling Non-Probability sampling

9. Probability sampling
Probability sampling is a sampling technique where a researcher sets a
selection of a few criteria and chooses members of a population randomly.
All the members have an equal opportunity to be a part of the sample with
this selection parameter.
It is fixed process
Considers every member in population.

10. EXAMPLE
• Let's assume in a population of 1000 members,
every member will have a 1/1000 chance of being
selected to be a part of a sample. Probability
sampling eliminates bias in the population and
gives all members a fair chance to be included in
the sample.

11. Probability Sample
1) Simple random sampling
2) Cluster sampling
3) Systematic Sampling
4) Stratified Random sampling

12. A) Simple random sampling
• It is a reliable method of obtaining information where every
single member of a population is chosen randomly, merely by
chance.
• Each individual has the same probability
• Saves time and resources of being chosen to be a part of a
sample.

13. Two techniques
•Lottery method
•Random numbers

14. Lottery method
• Using the lottery method is one of the
oldest ways and is a mechanical
example of random sampling. In this
method, the researcher gives each
member of the population a number.
Researchers draw numbers from the
box randomly to choose samples.

15. Use of random
numbers
• The use of random numbers is an alternative
method that also involves numbering the
population. The use of a number table like the one
below can help with this sampling technique.

16. Understating simple random sampling
• Follow these steps to extract a simple random sample of 100 employees
out of 500.
1.Make a list : of all the employees working in the organization. (as
mentioned above there are 500 employees in the organization, the record
must contain 500 names).
2.Assign a sequential number: to each employee (1,2,3…n). This is your
sampling frame (the list from which you draw your simple random
sample).
3.Figure out what your sample size is going to be. (In this case, the
sample size is 100).
4.Use a random number generator to select the sample, using your
sampling frame (population size) from Step 2 and your sample size from
Step 3. For example, if your sample size is 100 and your population is 500,
generate 100 random numbers between 1 and 500.

17. Calculation of simple random method
• POPULATION SIZE =N
• Sample size = n
oP=1-N-1/N.N-2/N-1…..N-n-/N-()n-1)
oCancelling=1-N-n/N
o=n/N
o =100/1000
o=10%

18. When to use Simple random sampling
You have a
complete list of
every member of
the population.
01
You can contact
or access each
member of the
population if they
are selected.
02
You have the time
and resources to
collect data from
the necessary
sample size.
03

19. How to perform Simple random sampling
Collect Collect data from your sample
Select Randomly select sample
Decide on Decide on the sample size
Define Define the population

20. 1. Define the Population
• Its important to ensure that you have acess to all the selected
population so that the data can be collected from each one of one of
them who are selected.

21. 2. Decide on the sample size
• Next, you need to decide how large your sample size will be. Although
larger samples provide more statistical certainty, they also cost more and
require far more work.
• but one of the simplest involves using a formula with your
desired confidence interval and confidence level, estimated size of the
population you are working with, and the standard deviation of whatever
you want to measure in your population.
• Common confidence level and confidence interval is 0.95 & 0.05
• https://www.qualtrics.com/au/experience-management/research/determine-sample-
size/?rid=ip&prevsite=uk&newsite=au&geo=IN&geomatch=au

22. 3 Randomly select your sample
This can be done in one of two ways
the lottery
random number method.

23. 4 Collect data from your sample
Make sure all the
sample population
submit their responses
If some have been
backdropped or not
answered than it cloud
lead to bias your
findings.

24. Advantages of
Simple random
sampling
Reduces bias
Easy to pick smaller sample size from existing large
population
It is a fundamental of data collection
No restrictions on sample size
The data collected through this sampling method is
well informed; more the samples better is the quality
of the data.

25. Demerits of
Randomization
1. It cannot ensure the
representativeness of a sample.
2. It does not use the knowledge
about the population.
3. Its inferential accuracy
depends upon the size of the
sample.

26. Cluster Sampling
In cluster sampling, researchers divide a population into smaller groups known
as clusters. They then randomly select among these clusters to form a sample.
Geographically dispersed.
Researchers usually use pre-existing units such as schools or cities as their
clusters.
Age, sex, location

27. How to
cluster
sample
Collect Collect data from the sample
Select Randomly select clusters to use as your
sample
Divide Divide your sample into clusters
Define Define your population

28. Example: You are interested in the average reading level of all the seventh-
graders in your city.
• It would be very difficult to obtain a list of all seventh-graders and
collect data from a random sample spread across the city. However, you
can easily obtain a list of all schools and collect data from a subset of
these. You thus decide to use the cluster sampling method.

29. 1. Define the
Population
•Seventh graders

30. 2. Divide your
sample into
clusters
Each cluster’s population should be as diverse as possible.
You want every potential characteristic of the entire
population to be represented in each cluster.
Each cluster should have a similar distribution of
characteristics as the distribution of the population.
Taken together, the clusters should cover the entire
population.
There not be any overlap between clusters (i.e., the same
people or units do not appear in more than one cluster).

31. Clusters
• You cluster the seventh-graders by the school
they attend. To cover the whole population,
you need to include every school in the city.
There is no overlap because each student
attends only one school.

32. 3 Randomly select clusters
to use as your sample
• Each cluster is itself a mini-representation of
the larger population, randomly selecting and
sampling from the clusters allows you to
imitate simple random sampling which
supports validity results

33. 4 Collect data
from the sample
• You test the reading levels of every
seventh-grader in the schools that
were randomly selected for your
sample.

34. Merits
It may be a good representative of the population.
It is an easy method.
It is an economical method.
It is practicable and highly applicable in education.
Observations can be used for inferential purpose.

35. Demerits
Cluster sampling is not
free from errors.
It is not comprehensive.

36. Systematic sampling
• Systematic sampling is a probability sampling method in which researchers
select members of the population at a regular interval (or k) determined in
advance.
• Order of the population

37. • Your population list alternates between men (on the even numbers) and women
(on the odd numbers). You choose to sample every tenth individual, which will
therefore result in only men being included in your sample. This would obviously
be unrepresentative of the population.

38. Sampling Without Known Population
• You run a department store and are interested in how you can improve the store
experience for your customers. To investigate this question, you ask an employee
to stand by the store entrance and survey every 20th visitor who leaves, every day
for a week.
• Although you do not necessarily have a list of all your customers ahead of time,
this method should still provide you with a representative sample of your
customers since their order of exit is essentially random.

39. 1.Define your population
• You can select your sample ahead of time from a list and then
approach the selected subjects to collect data,
or
• You can approach every kth member of your target
population to ask them to participate in your study
• Listing the population in advance
• Selecting your sample on the spot

40. 2 Decide on your
sample size and
sampling interval
• Before you choose your interval, you
must first decide on your sample size.

41. • Sample size and sampling interval Although you do not know exactly
how many people will visit your store ahead of time, you can estimate
the total population by using an average of the prior few weeks’ foot
traffic.
• You estimate that around 7500 people visit your store each week and
based on this estimate you calculate an ideal sample size of 366. Your
sampling interval k thus equals 7500/366 = 20.49, which you round to
20.

42. • 7500= Population size (N)
• 366= Sample size (n)
• (95% of confidence level & 0.05Confidence interval and we also have
margin of error )
K=Regular interval
N= Population
n= sample size
K=N/n K= 7500/366 = 20.49 K=20 or 21

43. 3 Select the sample and collect data
• If you already have a list of your population, randomly select a starting point on
your list, and from there, select every kth member of the population to include in
your sample.
• If you don’t have a list, you choose every kth member of the population for your
sample at the same time as collecting the data for your study.

44. When to use systematic
sampling
Budget
Restrictions
Uncomplicated
Implementation
Absence of data
pattern
Low risk data
manipulation in
research

45. What are the types of systematic
sampling?
Systematic
random
sampling
Linear
systematic
sampling
Circular
systematic
sampling

46. Systematic random sampling
• Systematic random sampling is a method to select samples at a
particular preset interval. As a researcher, select a random starting
point between 1 and the sampling interval. Below are the example steps
to set up a systematic random sample

47. 1.First, calculate and fix the sampling interval. (The number of elements in the
population divided by the number of elements needed for the sample.)
2.Choose a random starting point between 1 and the sampling interval.
3.Lastly, repeat the sampling interval to choose subsequent elements.

48. Linear systematic sampling
• Rather than selecting these ‘n’ units of a sample randomly, a researcher can apply
a skip logic to select these. It follows a linear path and then stops at the end of a
particular population.

49. Calculation of linear systematic sampling
• Arrange the entire population in a classified sequence.
• Select the sample size (n)
• Calculate sampling interval (k) = N/n
• Select a random number between 1 to k (including k)
• Add the sampling interval (k) to the chosen random number to add the next
member to a sample and repeat this procedure to add remaining members of the
sample.
• In case k isn’t an integer, you can select the closest integer to N/n.

50. • This sampling or skip interval (k) = N (total population units)
n (sample size)

51. Circular systematic sampling
• In circular systematic sampling, a sample starts again from the same point
once again after ending; thus, the name.
• Example, if N = 7 and n = 2 K=?

52. Calculation of circular systematic
sampling
• Given : N = 7 and n = 2 K=?
• K=N/n
• K=7/2
• K=3.5 (Because of decimal we got 2 probability i.e., 3rd and 4th
individual)
• Two Probabilities here
• Let k be 3rd individual population
• Let k be 4th individual of populations

53. 4
BE
CG
DA
3
BD
CE
DF
EG
A
B
C
D
E
F
G

54. • Calculate sampling interval (k) = N/n. (If N = 11 and n = 2, then k is taken as 5
and not 6)
• Start randomly between 1 to N
• Create samples by skipping through k units every time until you select members
of the entire population.
• In the case of this method, there will be N number of samples, unlike k samples
in the linear systematic sampling method.

55. Linear Systematic Sampling Circular Systematic Sampling
Create samples = k (sampling interval) Create samples = N (total population)
The start and endpoints of this sample are distinct.
It restarts from the start point once the entire population is
considered.
All sample units should be arranged in a linear manner
prior to selection.
Elements will be arranged in a circular manner.

56. Stratified Sample
• In a stratified sample, researchers divide a
• Population into homogeneous subpopulations called strata
• Based on specific characteristics (e.g., race, gender identity, location, etc.).
• Every member of the population studied should be in exactly one stratum.
• Each stratum is then sampled using another probability sampling method
• Researchers rely on stratified sampling when a population’s characteristics are
diverse, and they want to ensure that every characteristic is properly represented.

57. Stratified random sampling properties.
• The population consists of N elements.
• The population is divided into h groups, called strata.
• Each element of the population can be assigned to one, and
only one, stratum.
• The number of observations within each stratum nh is known,
and n = n1 + n2 + n3 + ... + nh-1 + nh.
• The researcher obtains a probability sample from each
stratum.

58. N=28
Population (N)
A B B C D E F G A A
B C C B B A B C A D
E E F E A F A A F G

59. 6 Strata (h)
Strata
MASTERS IN
COMMERCE
B B B B B B MASTERS IN SCIENCE
C C C C BACHELOR’S DEGREE IN COMMERCE
E E E E BACHELOR’S DEGREE IN SCIENCE
F F F F BACHELOR’S DEGREE IN ARTS
G G PhD in commerce

60. Random selection
A B D E F G A B D E
F A B A E A B C A B

61. Sample
A B C D E F

62. When to use stratified sampling
Ensuring
• Ensuring the diversity of your sample
Similar
• Ensuring similar variance
Lowering
• Lowering the overall variance in the population
Allowing
• Allowing for a variety of data collection methods

63. Research example
• You are interested in how having a doctoral degree affects the wage gap
between gender identities among graduates of a certain university.
• Because only a small proportion of this university’s graduates have
obtained a doctoral degree, using a simple random sample would likely
give you a sample size too small to properly compare the differences
between men, women, and those who do not identify as men or women
with a doctoral degree versus those without one.
• Therefore, you decide to use a stratified sample, relying on a list
provided by the university of all its graduates within the last ten years.

64. How to perform
Define
• Define your population and subgroups
Separate
• Separate the population into strata
Decide on
• Decide on the sample size for each stratum
Sample
• Randomly sample from each stratum

65. Step 1: Define your population and
subgroups
Choosing characteristics for stratification
Stratifying by multiple characteristics
EX: Your population is all graduates of the university within the
last ten years. You will stratify by both gender identity and
degree received.

66. Step 2: Separate the population into
strata
• collect a list of every member of the population and
assign each member to a stratum.
• There is no overlap between them.
• EX: You compile a list of every graduate’s name, gender
identity, and the degree that they obtained. Using this
list, you stratify on two characteristics: gender identity,
with three strata (male, female, and other), and degree,
with three strata (bachelor’s, master’s, and doctorate).

67. Characteristic Strata Groups
Gender Identity •Female
•Male
•Other
1.Male bachelor’s graduates,
2.Female bachelor’s graduates,
3.Other bachelor’s graduates
4.Male master’s graduates,
5.Female master’s graduates,
6.Other master’s graduates
7.Male doctoral graduates,
8.Female doctoral graduates,
9.Other doctoral graduates
Degree •Bachelor’s
•Master’s
•Doctorate

68. Step 3: Decide on the sample size for
each stratum
• First, you need to decide whether you want your sample to be
proportionate or disproportionate.
• In proportionate sampling, the sample size of each stratum is
equal to the subgroup’s proportion in the population as a whole.
• In disproportionate sampling, the sample sizes of each strata
are disproportionate to their representation in the population as
a whole.
• Sample size should be large enough to ensure you can draw
statistical conclusions about each subgroup.

69. Step 4: Randomly sample from each
stratum
• Finally, you should use another probability sampling
method, such as simple random or systematic sampling,
to sample from within each stratum.
• If properly done, the randomization inherent in such
methods will allow you to obtain a sample that is
representative of that particular subgroup.

70. Example
• You use simple random sampling to choose subjects
from within each of your six groups, selecting a roughly
equal sample size from each one.
• You can then collect data on salaries and job histories
from each of the members of your sample to investigate
your question.

71. Advantages
• A stratified sample can provide greater precision than a simple
random sample of the same size.
• Because it provides greater precision, a stratified sample often
requires a smaller sample, which saves money.
• A stratified sample can guard against an "unrepresentative"
sample (e.g., an all-male sample from a mixed-gender
population).
• We can ensure that we obtain sufficient sample points to
support a separate analysis of any subgroup.

72. Disadvantages
• It may require more administrative effort than a simple random
sample.
• The analysis is computationally more complex.

73. NON-PROBABILITY SAMPLING
• Non-probability sampling is a sampling method in which not all members of the
population have an equal chance of participating in the study, unlike probability
sampling.
• useful for exploratory studies like a pilot survey (deploying a survey to a smaller
sample compared to pre-determined sample size).
• Researchers use this method in studies where it is impossible to draw random
probability sampling due to time or cost considerations.

74. Types of non-probability sampling
Convenience sampling
Consecutive sampling
Quota sampling
Judgmental or Purposive sampling
Snowball sampling

75. convenience
sampling
• Convenience sampling is defined as a method adopted by
researchers where they collect market research data
from a conveniently available pool of respondents.
• In many cases, members are readily approachable to be
a part of the sample.
• Large Population
• irrespective of generalization.

76. Applications
• Applied by brands and organizations to measure their perception of their
image in market
• To know new product launch
• For example, a university student working on a project and wants to
understand the average consumption of soda on campus on a Friday
night will most possibly call his/her classmates and friends and ask how
many cans of soda they consume. Or may go to a party nearby and
conduct an easy survey. There is always a chance that the randomly
selected population may not accurately represent the population of
interest, thus increasing the chances of bias

77. Top Six
Advantages
Of Using
Convenience
Sampling
Collect data quickly
Inexpensive to create samples
Easy to do research
Low cost
Readily available sample
Fewer rules to follow

78. 1. Collect
data
quickly:
In situations where time is a constraint, many researchers
choose this method for quick data collection. The rules to
gather elements for the sample are least complicated in
comparison to techniques such as simple random
sampling, stratified sampling, and systematic sampling.
Due to this simplicity, data collection takes minimal time.

79. 2.
Inexpensiv
e to create
samples
The money and time invested in
other probability sampling
methods are quite large compared
to convenience sampling.
It allows researchers to generate
more samples with less or no
investment and in a brief period.

80. 3. Easy to
do
research:
• The name of this surveying technique clarifies how
samples are formed. Elements are easily accessible by
the researchers and so, collecting members for the
sample becomes easy.

81. 4.Low cost
• Low cost is one of the main reasons why researchers adopt
this technique.
• When on a small budget, researchers – especially students,
can use the budget in other areas of the project.

82. 5.Readily
Available Sample
Data collection is easy and accessible. Most convenience
sampling considers the population at hand. Samples are readily
available to the researcher. They do not have to move around too
much for data collection. Quotas are met quickly, and the data
collection can commence even within a few hours.

83. Fewer rules to
follow
It doesn’t require going through a checklist to filter members of
an audience. Here, gathering critical information and data
becomes uncomplicated.
For instance, if an NGO wants to survey women’s
empowerment, they can go to schools, colleges, offices, etc. in
their proximity and gather quick responses.

84. Disadvantages
of Convenience
Sampling
1. A convenience sample doesn’t provide a representative
result:
2. A convenience sample doesn’t provide a representative
result.
3. Some researchers don’t understand the specificity of
convenience sampling.
4. It is challenging to replicate the results of convenience
samples.
5. Researcher bias can enter into the sampling technique.

85. How to reduce bias in
convenience sampling?
• Use it with probability sampling
• Mix of both convenience sampling and
probability.

86. How to efficiently
analyze convenience
sampling data?
Take Multiple samples. It
helps you in producing
reliable results.
Repeat the survey to
understand whether
your results truly
represent the
population.
For a big sample size, try
cross-validation for half
the data. Then compare
the findings with the
other half of the data.

87. Consecutive sampling
• This non-probability sampling method is very similar to convenience
sampling, with a slight variation. Here, the researcher picks a single
person or a group of a sample, conducts research over a period,
analyzes the results, and then moves on to another subject or group
if needed.
• Consecutive sampling technique gives the researcher a chance to
work with many topics and fine-tune his/her research by collecting
results that have vital insights.

88. Unique Characteristics
• consecutive sampling, there is a third option available. Here, a researcher can
accept the null hypothesis, if not the null hypothesis, then its alternative
hypothesis. If neither of them is applicable, then a researcher can select another
pool of samples and conduct the research or the experiment once again before
finally making a research decision.

89. Consecutive sampling example
• Here is an easy-to-understand example of consecutive sampling
• One of the most common examples of a consecutive sample is when companies/ brands stop people in a mall or crowded areas and
hand them promotional leaflets to purchase a luxury car.
• In this example, the people walking in the mall are the samples, and let us consider them as representative of a population.
• Now, the researcher hands these people an advertisement or a promotional leaflet. A few of them agree to stay back and respond to
the questions asked by the promotion executive (we can consider him/her as a researcher).
• The responses are collected and analyzed, but there is no conclusive result that people would want to buy that car based on the
features described in the leaflet.
• The promotion executive now asks questions to another group of people who analyze the details of the car and its features and show
a keen interest in buying the luxury car. Thus, this group of people has provided conclusive results for purchasing the vehicle.

90. Advantages
Cost effective Less time
consuming
Valuable Room for
improvement

91. Cost effective
This sampling method does not require the organizations to
hire separate professionals to gather research data .
The researcher also saves money by avoiding hiring agencies
that find them suitable respondents for their study .
This allows the researchers to make cost cuts .

92. Less time consuming
Since the sample is selected based on
convenience , not much time is wasted on
looking for respondents .
This allows the researchers to gather fast
data and leaves sufficient time for analysis
without causing any haste .

93. Valuable
• Consecutive sampling is the most valuable method of sampling as it
guarantees results .
• The researcher can move from one sample to another in order to
satisfy the research purpose as well as verifying previously obtained
data .
• This ensures both , value for money and effort.

94. Room for improvement
• Consecutive sampling allows the researcher to recognize and correct their
mistakes in order to improve future results.
• As in the above example , the shoe brand ( in this case, the researching
organization) was able to correct and modify their survey to avoid
repeating mistakes made in the first round and make the study reach more
conclusive results .

95. Disadvantages
Sampling bias
Time in designing &
Conduction of survey

96. Sampling bias
• Although the method of sample selection is less time consuming and
reduces effort , it can impact the quality of the results obtained .
• Moreover , there are chances that a particular sect of the population did
not find representation in the sample .

97. Time in designing & Conduction of survey
• Consecutive sampling saves the researchers time in terms of sample
selection, but it takes a lot of time in designing and redesigning the
survey to avoid repeating the same results esp.

98. 3. Quota sampling
Quota sampling is defined as a non-probability sampling method in which researchers
create a sample involving individuals that represent a population.
Researchers choose these individuals according to specific traits or qualities.
They decide and create quotas so that the market research samples can be useful in
collecting data.
These samples can be generalized to the entire population.
The final subset will be decided only according to the interviewer’s or researcher’s
knowledge of the population.

99. Example
• A cigarette company wants to find out what age group
prefers what brand of cigarettes in A particular city.
He/she applies quotas on the age groups of 21-30, 31-
40, 41-50, and 51+. From this information, the
researcher gauges the smoking trend among the
population of the city.

100. Types of quota
sampling:
• Controlled quota sampling:
Controlled quota sampling imposes restrictions on the
researcher’s choice of samples. Here, the researcher is limited
to the selection of samples.
• Uncontrolled quota sampling:
Uncontrolled quota sampling does not impose any restrictions
on the researcher’s choice of samples. Here, the researcher
chooses sample members at will.

101. How to perform quota
sampling:
1.Divide the sample population into
subgroups: With stratified sampling, the
researcher bifurcates the entire population into
mutually exhaustive subgroups, i.e., the elements
of each of the subgroups becomes a part of only
one of those subgroups. Here, the researcher
applies random selection.

102. 2.Figure out the weightage of subgroups:
The researcher evaluates the proportion in which the subgroups
exist in the population. He/she maintains this proportion in the
sample selected using this type of sampling method.
For example, if 58% of the people who are interested in
purchasing your Bluetooth headphones are between the age
group of 25-35 years, your subgroups also should have the same
percentages of people belonging to the respective age group.

103. 3.Select an appropriate
sample size
The researcher should select the sample size while
maintaining the proportion evaluated in the previous step. If
the population size is 500, the researcher can pick a sample
of 50 elements.
The sample chosen after following the first three steps should
represent the target population.

104. 4. Conduct surveys according to the quotas
defined
• Make sure to stick to the predefined quotas to achieve actual
actionable results. Don’t survey quotas that are full and focus
on completing surveys for each quota.

105. Characteristics of quota sampling:
1. Aims to get the best representation of respondents in the final sample.
2.Quotas replicate the population of interest in a real sense.
3.The estimates produced are more representative.
4.The quality of quota samples vary.
5.Saves research data collection time as the sample represents the population.
6.Saves research costs if the quotas accurately represent the population.
7.It monitors the number of types of individuals who take the survey.
8.The researcher always divides the population into subgroups.
9.The sample represents the entire population.
10.Researchers use the sampling method to identify the traits of a specific group of people.

106. Advantages of quota sampling
Saves time: Because of the involvement of a quota for sample creation, this sampling process is quick
and straightforward.
Research convenience: By using quota sampling and appropriate research questions, interpreting
information and responses to the survey is a much convenient process for a researcher.
Accurate representation of the population of interest: Researchers effectively represent a
population using this sampling technique. There is no room for over-representation as this sampling
technique helps researchers to study the population using specific quotas.
Saves money: The budget required for executing this sampling method is minimalistic.

107. 4. Snowball Sampling: Definition
• Snowball sampling or chain-referral sampling is defined as a non-probability
sampling technique in which the samples have traits that are rare to find.
• This is a sampling technique, in which existing subjects provide referrals to
recruit samples required for a research study.
• This sampling method involves a primary data source nominating other
potential data sources that will be able to participate in the research studies.
• Snowball sampling method is purely based on referrals and that is how a
researcher can generate a sample.
• Therefore, this method is also called the chain-referral sampling method.

108. For example
• If you are studying the level of customer satisfaction among the members of an
elite country club, you will find it extremely difficult to collect primary
data sources unless a member of the club agrees to have a direct conversation
with you and provides the contact details of the other members of the club.

109. A
B
A1
A3
A4
A5
B2
B1 B3
B4
B5
B6

110. Errors in Sampling:
• Sampling Error
• Sampling error refers to differences between the sample and the population
that exist only because of the observations that happened to be selected for the
sample
• Increasing the sample size will reduce this type of error

111. Types of
Sampling
Error
Sample
Errors
Non-Sample
Errors

112. Sample Errors
Error caused by the act of taking a sample
They cause sample results to be different from
the results of census
Differences between the sample and the
population that exist only because of the observations
that happened to be selected for the sample
Statistical Errors are sample error (Type I & Type II
Error)
We have no control over

113. Non-Sample Errors
Not Control by Sample Size
• Non-Response Error
• Response Error

114. Non-Response Error
• A non-response error occurs when units selected as part of the sampling
procedure do not respond in whole or in part

115. Respondent error
• Respondent gives an incorrect answer, e.g., due to prestige or
competence implications, or due to sensitivity or social undesirability of
question
• Respondent misunderstands the requirements
• lack of motivation to give an accurate answer
• “lazy” respondent gives an “average” answer
• question requires memory/recall
• Proxy respondents are used, i.e. taking answers from someone other
than the respondent

116. Interviewer bias
• Different interviewers administer a survey in different ways
• Differences occur in reactions of respondents to different
interviewers, e.g. to interviewers of their own sex or own ethnic
group
• Inadequate training of interviewers
• Inadequate attention to the selection of interviewers
• There is too high a workload for the interviewer

117. Measurement Error
• The question is unclear, ambiguous or difficult to answer
• The list of possible answers suggested in the recording
instrument is incomplete
• Requested information assumes a framework unfamiliar to
the respondent
• The definitions used by the survey are different from those
used by the respondent (e.g. how many part-time employees
do you have?)

118. Key Points on Errors
• Non-sampling errors are inevitable in production of national statistics.
Important that:-
• At planning stage, all potential non-sampling errors are listed, and steps
taken to minimize them are considered.
• If data are collected from other sources, question procedures adopted for
data collection, and data verification at each step of the data chain.
• Critically view the data collected and attempt to resolve queries
immediately they arise.
• Document sources of non-sampling errors so that results presented can
be interpreted meaningfully.

119. 2. Judgmental or purposive sampling:
• Purposive sampling (also known as judgment, selective or subjective sampling)
• It is a sampling technique in which researcher relies on his or her own judgment when choosing
members of population to participate in the study.
• Researchers often believe that they can obtain a representative sample by using a sound judgment,
which will result in saving time and money”.
• TV reporters stopping certain individuals on the street in order to ask their opinions about certain
political changes constitutes the most popular example of this sampling method. However, it is
important to specify that the TV reporter must apply certain judgment when deciding who to stop
on the street to ask questions; otherwise, it would be the case of random sampling technique.

120. Alternatively, purposive sampling method may prove to be effective when only limited
numbers of people can serve as primary data sources due to the nature of research
design and aims and objectives.
For example, for a research analyzing affects of personal tragedy such as family
bereavement on performance of senior level managers the researcher may use his/her
own judgment in order to choose senior level managers who could particulate in in-
depth interviews.

121. Advantages Of Judgement Sampling
a) The approach is well
understood and has been refined
by experience over many years;
b) The auditor is given an
opportunity to bring his
judgement and expertise into
play. After all auditing is an
exercise in professional
judgement;
c) No special knowledge of
statistics is required;
d) No time is wasted playing with
mathematics;

122. Disadvantages
a) It is unscientific;
b) It is wasteful and usually too large samples are selected;
c) You cannot extrapolate the results to the population as the samples are not
representative;
d) Personal bias in selecting the sample is unavoidable;
e) There is no logic to the selection of the sample or its size;
f) The sample selection is so erratic that it cannot be said to have applied to all
items in the year;
g) the conclusions reached are usually vague.

123. When to Use Judgmental Sampling
• Judgmental sampling design is usually used when a limited number of individuals
possess the trait of interest.
• It is the only viable sampling technique in obtaining information from a very
specific group of people.
• It is also possible to use judgmental sampling if the researcher knows a reliable
professional or authority that he thinks is capable of assembling a representative
sample.

124. Advantages of Sampling
1. It has a greater adaptability.
2. It is an economical technique.
3. It has high speed for generalization.
4. According to W.G. Cocharan, “It has greater precision and
accuracy in the observation”.
5. This technique has great accuracy.
6. It has a greater speed in conducting a research work.
7. It has a greater scope in the field of research.
8. It reduces the cost of observation or data collection.

125. Disadvantages or Limitation of
Sampling
1. Scope of biasness.(Less accuracy)
2. Problem of representative sample-Difficulty in selecting a truly
representative sample.
3. Need of eligible researchers.
4. Instability of sample subjects or changeability of units i.e., in
heterogeneous population.
5. There are certain situations where sampling is possible.

126. BASIS FOR COMPARISON PROBABILITY SAMPLING NON-PROBABILITY SAMPLING
Meaning
Probability sampling is a sampling technique, in
which the subjects of the population get an equal
opportunity to be selected as a representative
sample.
Nonprobability sampling is a method of sampling
wherein, it is not known that which individual from
the population will be selected as a sample.
Alternately known as Random sampling Non-random sampling
Basis of selection Randomly Arbitrarily
Opportunity of selection Fixed and known Not specified and unknown
Research Conclusive Exploratory
Result Unbiased Biased
Method Objective Subjective
Inferences Statistical Analytical
Hypothesis Tested Generated