A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.

1. Sampling
PRESENTED BY:
SID G

2. Meaning
In research terms a sample is a group
of people, objects, or items that are
taken from a larger population for
measurement. The sample should be
representative of the population to
ensure that we can generalise the
findings from the research sample to
the population as a whole.

3. • A sample design is a definite plan for obtaining a
sample from a given population. It refers to the
technique or the procedure the researcher would
adopt in selecting items for the sample. Sample
design may as well lay down the number of items to
be included in the sample i.e., the size of the
sample. Sample design is determined before data
are collected. There are many sample designs from
which a researcher can choose. Some designs are
relatively more precise and easier to apply than
others. Researcher must select/prepare a sample
design which should be reliable and appropriate for
his research study.

4. CONCEPT
• POPULATION: In statistics, the term population is used
to describe the subjects of a particular study—
everything or everyone who is the subject of a
statistical observation. Populations can be large or small
in size and defined by any number of characteristics
• SAMPLE:
a sample is a subset of a population that is used to
represent the entire group as a whole. When doing
research, it is often impractical to survey every member
of a particular population because the sheer number of
people is simply too large. To make inferences about the
characteristics of a population

5. • SAMPLING FRAME The sampling frame is the list from
which the potential respondents are drawn
Telephone directory
List of five star Hotel
List of student
”which is a list of all the units of the population of
interest. You can only apply your research findings to
the population defined by the sampling frame”
For example
Population: Birds that are pink.
Sampling Frame:
Brown-capped Rosy-Finch.
White-winged Crossbill.
American Flamingo.
Roseate Spoonbill.
Black Rosy-Finch.
Cassin’s Finch.

6. • SAMPLE SIZE Before deciding how large a sample
should be, you have to define your study population (who
you are including and excluding in your study). The
question of how large a sample should be is a difficult one.
Sample size can be determined by various constraints
(funding available, the time constraints etc.) Sample size
depends on
• • The type of data analysis to be performed
• • The desired precision of the estimates one wishes to
achieve
• • The kind and number of comparisons that will be made
• •The number of variables that have to be examined
simultaneously
• • How heterogeneous the sampled population is.

7. • Deciding on a sample size for qualitative
inquiry can be even more difficult than
quantitative because there are no definite
rules to be followed. It will depend on what
you want to know, the purpose of the
inquiry, what is at stake, what will be
useful, what will have credibility and what
can be done with available time and
resources. You can choose to study one
specific phenomenon in depth with a
smaller sample size or a bigger sample size
when seeking breadth

8. Sampling theory is designed to attain
one or more of the following objectives:
• Statistical estimation: Sampling theory helps in
estimating unknown population parameters from a
knowledge of statistical measures based on sample
studies. In other words, to obtain an estimate of
parameter from statistic is the main objective of the
sampling theory. The estimate can either be a point
estimate or it may be an interval estimate. Point
estimate is a single estimate expressed in the form of
a single figure, but interval estimate has two limits
viz., the upper limit and the lower limit within which
the parameter value may lie. Interval estimates are
often used in statistical induction.

9. • Testing of hypotheses: The second objective of
sampling theory is to enable us to decide
whether to accept or reject hypothesis; the
sampling theory helps in determining whether
observed differences are actually due to chance
or whether they are really significant.
• Statistical inference: Sampling theory helps in
making generalization about the population/
universe from the studies based on samples
drawn from it. It also helps in determining the
accuracy of such generalizations.

10. NEED OF SAMPLE(PURPOSE)
• To draw conclusions about populations
from samples, we must use inferential
statistics, to enable us to determine a
population’s characteristics by directly
observing only a portion (or sample) of
the population. We obtain a sample of
the population for many reasons as it is
usually not practical and almost never
economical.

11. There would also be difficulties
measuring whole populations
because: -
• The large size of many populations
• Inaccessibility of some of the population - Some populations are
so difficult to get access to that only a sample can be used. E.g.
prisoners, people with severe mental illness, disaster survivors
etc. The inaccessibility may be associated with cost or time or
just access.
• Destructiveness of the observation- Sometimes the very act of
observing the desired characteristic of the product destroys it for
the intended use. Good examples of this occur in quality control.
E.g. to determine the quality of a fuse and whether it is defective,
it must be destroyed. Therefore if you tested all the fuses, all
would be destroyed.
• Accuracy and sampling - A sample may be more accurate than
the total study population. A badly identified population can
provide less reliable information than a carefully obtained
sample.

12. GOOD SAMPLE
• The Features of good Sampling are Stated below
• Sample design must result in a truly representative
sample.
• Sample design must be such which results in a
small sampling error.
• Sample design must be viable in the context of
funds available for the research study.
• Sample design must be such so that systematic bias
can be controlled in a better way.
• Sample should be such that the results of the
sample study can be applied, in general, for the
universe with a reasonable level of confidence.

14. TYPES OF SAMPLING
• Sampling is defined as the process of selecting
certain members or a subset of the population
to make statistical inferences from them and
to estimate characteristics of the whole
population. Sampling is widely used by
researchers in market research so that they do
not need to research the entire population to
collect actionable insights. It is also a time-
convenient and a cost-effective method and
hence forms the basis of any research design.

15. Any market research study requires two
essential types of sampling. They are:
• Probability Sampling: Probability sampling s a sampling
method that selects random members of a population by
setting a few selection criteria. These selection
parameters allow every member to have the equal
opportunities to be a part of various samples.
• Non-probability Sampling: Non probability sampling
method is reliant on a researcher’s ability to select
members at random. This sampling method is not a fixed
or pre-defined selection process which makes it difficult
for all elements of a population to have equal
opportunities to be included in a sample.

16. Probability Sampling
• is a sampling technique in which sample from
a larger population are chosen using a method
based on the theory of probability. This
sampling method considers every member of
the population and forms samples on the basis
of a fixed process. For example, in a
population of 1000 members, each of these
members will have 1/1000 chances of being
selected to be a part of a sample. It gets rid of
bias in the population and gives a fair chance
to all members to be included in the sample.

17. Simple Random Sampling
One of the best probability sampling techniques that helps in
saving time and resources, is the Simple Random
Sampling method. It is a trustworthy method of obtaining
information where every single member of a population is
chosen randomly, merely by chance and each individual has
the exact same probability of being chosen to be a part of a
sample.
For example, in an organization of 500 employees, if the HR
team decides on conducting team building activities, it is
highly likely that they would prefer picking chits out of a
bowl. In this case, each of the 500 employees has an equal
opportunity of being selected.

18. • Advantage
Easy method to use
No need of prior information of population
Equal and independent chance of selection to
every element
• Disadvantages
If sampling frame large, this method
impracticable.
Does not represent proportionate.

19. Systematic Sampling:
• Using systematic sampling method, members of a sample
are chosen at regular intervals of a population. It requires
selection of a starting point for the sample and sample size
that can be repeated at regular intervals. This type of
sampling method has a predefined interval and hence this
sampling technique is the least time-consuming.
• For example, a researcher intends to collect a systematic
sample of 500 people in a population of 5000. Each
element of the population will be numbered from 1-5000
and every 10th individual will be chosen to be a part of the
sample (Total population/ Sample Size = 5000/500 = 10).

20. • ADVANTAGES:
Sample easy to select
Suitable sampling frame can be identified easily
Sample evenly spread over entire reference population
Cost effective
• DISADVANTAGES:
Sample may be biased if hidden periodicity in
population coincides with that of selection.
Each element does not get equal chance
Ignorance of all element between two n element
Systematic Sampling

21. Stratified Random Sampling:
• Stratified Random sampling is a method where the population
can be divided into smaller groups, that don’t overlap but
represent the entire population together. While sampling, these
groups can be organized and then draw a sample from each group
separately.
• For example, a researcher looking to analyze the characteristics
of people belonging to different annual income divisions, will
create strata (groups) according to annual family income such as
– Less than $20,000, $21,000 – $30,000, $31,000 to $40,000,
$41,000 to $50,000 etc. and people belonging to different income
groups can be observed to draw conclusions of which income
strata have which characteristics. Marketers can analyze which
income groups to target and which ones to eliminate in order to
create a roadmap that would definitely bear fruitful results.

22. • Advantage :
Enhancement of representativeness to each sample
Higher statistical efficiency
Easy to carry out
• Disadvantage:
Classification error
Time consuming and expensive
Prior knowledge of composition and of distribution
of population

23. Cluster Sampling
• Cluster sampling is a method where the researchers divide the
entire population into sections or clusters that represent a
population. Clusters are identified and included in a sample on
the basis of defining demographic parameters such as age,
location, sex etc. which makes it extremely easy for a survey
creator to derive effective inference from the feedback.
• For example, if the government of the United States wishes to
evaluate the number of immigrants living in the Mainland
US, they can divide it into clusters on the basis of states such as
California, Texas, Florida, Massachusetts, Colorado, Hawaii
etc. This way of conducting a survey will be more effective as
the results will be organized into states and provides insightful
immigration data.

24. Non-probability Sampling Methods
• The non-probability method is a sampling method that
involves a collection of feedback on the basis of a researcher
or statistician’s sample selection capabilities and not on a
fixed selection process. In most situations, output of a
survey conducted with a non-probable sample leads to
skewed results, which may not totally represent the desired
target population. But, there are situations such as the
preliminary stages of research or where there are cost
constraints for conducting research, where non-probability
sampling will be much more effective than the other type.
• There are 4 types of non-probability sampling which will
explain the purpose of this sampling method in a better
manner:

25. Convenience sampling
• This method is dependent on the ease of access to subjects such
as surveying customers at a mall or passers-by on a busy
street. It is usually termed as convenience sampling, as it’s
carried out on the basis of how easy is it for a researcher to get
in touch with the subjects. Researchers have nearly no
authority over selecting elements of the sample and it’s purely
done on the basis of proximity and not representativeness. This
non-probability sampling method is used when there are time
and cost limitations in collecting feedback. In situations where
there are resource limitations such as the initial stages of
research, convenience sampling is used. For example, startups
and NGOs usually conduct convenience sampling at a mall to
distribute leaflets of upcoming events or promotion of a cause
– they do that by standing at the entrance of the mall and
giving out pamphlets randomly.

26. • Advantage: A sample selected for ease of access,
immediately known population group and good
response rate.
• Disadvantage: cannot generalize findings (do not
know what population group the sample is
representative of) so cannot move beyond describing
the sample.
•Problems of reliability
•Do respondents represent the target population
•Results are not generalizable

27. Judgmental or Purposive
Sampling
• In judgmental or purposive sampling, the sample is
formed by the discretion of the judge purely considering
the purpose of study along with the understanding of
target audience. Also known as deliberate sampling, the
participants are selected solely on the basis of research
requirements and elements who do not suffice the
purpose are kept out of the sample. For instance, when
researchers want to understand the thought process of
people who are interested in studying for their master’s
degree.

28. The selection criteria will be: “Are you interested in
studying for Masters in …?” and those who respond
with a “No” will be excluded from the sample.
Advantages Based on the experienced person's
judgment
Disadvantages Cannot measure the
representativeness of the sample

29. Snowball sampling:
• Snowball sampling is a sampling method that
is used in studies which need to be carried out
to understand subjects which are difficult to
trace. For example, it will be extremely
challenging to survey shelter less people or
illegal immigrants. In such cases, using the
snowball theory, researchers can track a few
of that particular category to interview and
results will be derived on that basis. This
sampling method is implemented in situations
where the topic is highly sensitive

30. • Advantages
Identifying small, hard-to reach uniquely defined
target population
Useful in qualitative research
access to difficult to reach populations (other
methods may not yield any results).
• Disadvantages
Bias can be present
Limited generalizability
not representative of the population and will result
in a biased sample as it is self-selecting.

31. Quota sampling
In Quota sampling, selection of members in this
sampling technique happens on basis of a pre-
set standard. In this case, as a sample is
formed on basis of specific attributes, the
created sample will have the same attributes
that are found in the total population. It is an
extremely quick method of collecting samples.

32. • Advantages
Contains specific subgroups in the proportions
desired
May reduce bias
easy to manage, and quick
• Disadvantages
Dependent on subjective decisions
Not possible to generalize
only reflects population in terms of the quota,
possibility of bias in selection, no standard error
Types of Non probability Sampling Designs