shashank

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2. SAMPLING
Design and Procedures
PRAVIN DADMAL 2
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SHASHANK JAIN

3. What is sampling?
Sampling is the act, process, or technique of selecting a
suitable sample, or a representative part of a population for the
purpose of determining parameters or characteristics of the
whole population.
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4. Purpose of Sampling:
To draw conclusions about populations from samples, we must
use inferential statistics which enables us to determine a
population`s characteristics by directly observing only a
portion (or sample) of the population.
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5. Population:-
It is the aggregate of all the elements that share some common set of
characteristics and that comprise the universe for the purpose of the
marketing research problem .
Census:-
A census involves a complete enumeration of the elements of a
population.
Sample:-
A sample is a subgroup of the population selected for participation in the
study.
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6. Sample Vs. Census
Conditions Favoring the Use
Type of Study Sample Census
1. Budget Small Large
2. Time available Short Long
3. Population size Large Small
4. Variance in the characteristic Small Large
5. Cost of sampling errors Low High
6. Cost of nonsampling errors High Low
7. Nature of measurement Destructive Nondestructive
8. Attention to individual cases Yes No
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7. Design sampling procedure:-
1.Should a sample be taken?
2. If so what process should be followed?
3. What kind of sample should be taken?
4. How large should it be?
5. What can be done to control and adjust for nonresponsive
error?
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8. The Sampling Design Process
Define the target Population
Determine the Sampling Frame
Select Sampling Techniques
Determine the Sample Size
Execute the Sampling Process
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10. STEP-1
TARGET POPULATION
The target population is the collection of elements
or objects that possess the information required by
the researcher and about which inferences are to
be made. The target population should be defined
in terms of elements, sampling units, and time.
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11. STEP-2
SAMPLING FRAME
• Sampling frame: A representation of the element of the
target population. It consist of a list or set of direction for
identifying target population.
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12. STEP-3
SELECT A SAMPLING TECHQUINIQUE
• Bayesian approach:
A selection method in which the element are selected
sequentially. It is explicitly (clearly) incorporate prior
information about population parameter as well as cost and
probabilities associated with making a wrong decisions.
• Sample with replacement:
A sampling technique in which an element can be included in
the sample more than once.
• Sampling without replacement:
A sampling technique in which an element can not be
included In the sample more than once.
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13. STEP-4
DETERMINE THE SAMPLE SIZE
Number of elements to be included in a
study.
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14. SAMPLE SIZE
Important qualitative factors in determining the sample size
 Importance of the decision
 Nature of the research
 Number of variables
 Nature of the analysis
 Sample sizes used in similar studies
 Incidence rates
 Completion rates
 Resource constraints
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15. SAMPLE SIZES USED IN MARKETING
RESEARCH STUDIES
Type of Study Minimum Size Typical Range
Problem identification research 500 1,000-2,500
(e.g. market potential)
Problem-solving research (e.g. 200 300-500
pricing)
Product tests 200 300-500
Test marketing studies 200 300-500
TV, radio, or print advertising (per 150 200-300
commercial or ad tested)
Test-market audits 10 stores 10-20 stores
Focus groups 2 groups 4-12 groups

16. STEP-5
EXECUTE THE SAMPLING PROCEDURE
• It requires detailed specifications of how the sampling
design decisions with respect to the population, sampling
frame, sampling unit, sampling technique and sample size
are to be implemented.
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17. Classification of Sampling Techniques
Sampling Techniques
Nonprobability Probability
Sampling Techniques Sampling Techniques
Convenience Judgmental Quota Snowball Expert Purposive
Sampling Sampling Sampling Sampling sampling sampling
Simple Random Systematic Stratified Cluster Other Sampling
Sampling Sampling Sampling Sampling Techniques
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18. SAMPLING TECHNIQUES
• Non probability sampling: Sampling techniques that
do not use chance selection procedures. Rather they
rely on the personal judgment of the research.
• Probability sampling: A sampling procedure in
which each element of the population has a fixed
probabilistic chance of being selected for the sample.

19. • The difference between Non probability sampling
technique and probability sampling technique is that
Non probability sampling does not involves random
sampling and probability sampling does.
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20. USE OF SAMPLING TECHNIQUES
Non probability sampling technique: it is used in
concept test, packaged test, name test and copy test
where projections to be populations are usually not
needed.
Probability sampling technique: It is used when there
is a need for highly accurate estimates to market share or
sales volume for entire market. It generally employ
telephone interview , stratified and systematic sampling
are combined with some form of random digit dialing to
select the response.
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21. CONVENIENCE SAMPLING
( NON PROBABILITY SAMPLING TECHNIQUE)
Convenience sampling attempts to obtain a sample
of convenient elements. Often, respondents are
selected because they happen to be in the right place
at the right time.
 Mall intercept interviews without qualifying the
respondents
 “People on the street” interviews
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22. JUDGMENTAL SAMPLING
( NON PROBABILITY SAMPLING TECHNIQUE)
Judgmental sampling: It is a form of
convenience sampling in which the
population elements are selected based on the
judgment of the researcher.
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23. QUOTA SAMPLING
( NON PROBABILITY SAMPLING TECHNIQUE)
Quota sampling may be viewed as two-stage
restricted judgmental sampling.
 The first stage consists of developing control
categories, or quotas, of population elements.
 In the second stage, sample elements are selected
based on convenience or judgment.
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24. SNOWBALL SAMPLING
( NON PROBABILITY SAMPLING TECHNIQUE)
In snowball sampling, an initial
group of respondents is selected,
usually at random.
 After being interviewed, these
respondents are asked to
identify others who belong to
the target population of interest
 Subsequent respondents are
selected based on the referral.
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25. SIMPLE RANDOM SAMPLING
(PROBABILITY SAMPLING TECHNIQUE)
• Each element in the population
has a known and equal probability
of selection.
• Each possible sample of a given
size (n) has a known and equal
probability of being the sample
actually selected.
• This implies that every element is
selected independently of every
other element.
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26. SYSTEMATIC SAMPLING
(PROBABILITY SAMPLING TECHNIQUE)
• The sample is chosen by selecting a random
starting point and then picking every it element in
succession from the sampling frame.
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27. Systematic sampling

28. STRATIFIED SAMPLING
(PROBABILITY SAMPLING TECHNIQUE)
• A two-step process in which
the population is partitioned
into subpopulations, or
strata.
• The strata should be
mutually exclusive and
collectively exhaustive in
that every population
element should be assigned
to one and only one stratum
and no population elements
should be omitted.
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29. • Next, elements are selected from each stratum by a
random procedure, usually SRS.
• A major objective of stratified sampling is to increase
precision without increasing cost.
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30. CLUSTER SAMPLING
(PROBABILITY SAMPLING TECHNIQUE)
• The target population is first
divided into mutually exclusive and
collectively exhaustive
subpopulations, or clusters.
• Then a random sample of clusters is
selected, based on a probability
sampling technique such as SRS.
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• 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).
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31. • Ideally, each cluster should be a small-scale
representation of the population.
• In probability proportionate to size sampling, the
clusters are sampled with probability proportional to
size. In the second stage, the probability of selecting
a sampling unit in a selected cluster varies inversely
with the size of the cluster.
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32. TYPES OF CLUSTER SAMPLING
Cluster Sampling
One-Stage Two-Stage Multistage
Sampling Sampling Sampling
Simple Cluster Probability
Sampling Proportionate
to Size Sampling
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33. OTHER PROBABILITY SAMPLING
TECHNIQUES
• SEQUENTIAL SAMPLING: A probability sampling
technique in which the population elements are sample
sequentially, data collection and analysis are done at each
stage and decision is made as to whether additional
population elements should be sampled.
• Double sampling: A sampling technique in which certain
population element are sampled twice.

34. Non Probability Sampling
Technique Strengths Weaknesses
Convenience Least expensive, least Selection bias, sample not
time-consuming, most representative, not recommended for
sampling convenient descriptive or causal research
Judgmental Low cost, convenient, Does not allow generalization,
sampling not time-consuming subjective
Quota Sample can be controlled Selection bias, no assurance of
sampling for certain characteristics representativeness
Snowball Can estimate rare Time-consuming
sampling characteristics
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35. Probability Sampling
Technique Strengths Weaknesses
Simple random Easily understood, Difficult to construct sampling
sampling frame, expensive, lower precision,
results projectable
no assurance of representativeness.
Systematic Can increase Can decrease representativeness
sampling representativeness,
easier to implement than
sampling frame not
necessary
Stratified Include all important Difficult to select relevant
sampling subpopulations, stratification variables, not feasible to
precision stratify on many variables, expensive
Cluster Easy to implement, cost Imprecise, difficult to compute and
sampling effective
interpret results
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36. THANK YOU
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