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MARK5811 AMR4
Sampling Strategies and Case Studies
T3-2023
Lecture structure for this week
• Course issues and questions
• Last topic: AMR3: Secondary Data Analysis
• Sampling design process
• Sampling techniques
• Case study design
• Next topic: AMR5: Interviewing
• Lecture summary
Population and sample
• A population is any group that shares a common set of traits,
e.g. All female directors in FMCG companies in Australia.
• A sample is a subgroup of the population selected for
participation in the study.
• Using populations is usually too expensive and logistically
impractical, so we use sampling.
Malhotra (2010)
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Quantitative Research
• Large samples
• Probability sampling
(random selection)
• Purpose: Generalisation
Qualitative Research
• Small samples
• Non-probability sampling
(purposive selection)
• Purpose: Rich and deep level
of understanding
Sampling: Quantitative vs Qualitative
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The Sampling design process
Define the target population
Determine the sampling frame
Select sampling technique(s)
Determine the sample size
Execute the sampling process
Define the target population
Target population refers to the ENTIRE group of individuals or
objects to which researchers are interested in generalizing the
conclusions.
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An example of the target population for a
household shopping behaviour research project
in Sydney
• Male or female head of the household who is responsible for
most of the household shopping at department stores.
• Sydney
• In the last 3-6 months
Determine the sampling frame
• In statistics, a sampling frame is the source material (e.g.
telephone book, a map, employee record) from which a sample
is drawn.
Customers
enrolled in
your loyalty
program.
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Classification of sampling techniques
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple Random
Sampling
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Convenience sampling
• 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.
• Involves gaining access to the most easily accessible subjects
such as:
➢Neighbours or people responding to a newspaper or internet
invitation to complete a survey
➢Mall intercept interviews without qualifying the respondents
➢ “People on the street” interviews
• Cheap and quick BUT low credibility.
A graphical illustration of convenience sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Group D happens to
assemble at a
convenient time and
place. So all the
elements in this group
are selected. The
resulting sample
consists of elements 16,
17, 18, 19 and 20. Note
that no elements are
selected from group A,
B, C, and E.
Judgmental/Purposive sampling
• Judgmental/purposive sampling is a form of convenience
sampling in which the population elements are selected based
on the judgment of the researcher. The researcher, exercising
judgment or expertise, chooses the elements to be included in
the sample, because he or she believes that they are
representative of the population of interest or are otherwise
appropriate, e.g.:
➢Test markets selected to determine the potential of a new
product.
➢Department stores selected to test a new merchandising
display system.
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A graphical illustration of judgmental sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
The researcher considers
groups B, C and E to be
typical and convenient.
Within each of these
groups one or two
elements are selected
based on typicality and
convenience. The
resulting sample consists
of elements 8, 10, 11, 13,
and 24. Note that no
elements are selected
from groups A and D.
Quota sampling
• 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.
Control Population Sample
Variable composition composition
Gender Percentage Percentage Number
Male 48 48 480
Female 52 52 520
____ ____ ____
100 100 1000
A graphical illustration of quota sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
A quota of one
element from each
group, A to E, is
imposed. Within each
group, one element is
selected based on
judgment or
convenience. The
resulting sample
consists of elements 3,
6, 13, 20 and 22.
Note, one element is
selected from each
column or group.
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Snowball sampling
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 referrals.
A graphical illustration of snowball sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Elements 2 and 9 are
selected randomly from
groups A and B. Element
2 refers elements 12 and
13. Element 9 refers
element 18. The resulting
sample consists of
elements 2, 9, 12, 13, and
18. Note that no element
is selected from group E.
Random
Selection Referrals
Simple random sampling
• Each element in the population has an equal probability of
being selected.
• This implies that every element is selected independently of
every other element.
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A graphical illustration of simple random sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Select five random
numbers from 1 to
25. The resulting
sample consists of
population elements
3, 7, 9, 16, and 24.
Note, there is no
element from Group
C.
Systematic sampling
• The sample is chosen by selecting a random starting point and
then picking every ith element in succession from the sampling
frame.
• The sampling interval, i, is determined by dividing the
population size N by the sample size n and rounding to the
nearest integer.
• 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, a random starting point/number is 23, the sample
consists of elements 23, 123, 223, 323, 423, 523, and so on.
A graphical illustration of systematic sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Select a random number
between 1 and 5, say 2.
If the sampling interval,
i, is 5.
The resulting sample
consists of population 2,
(2+5=) 7, (2+5x2=) 12,
(2+5x3=)17, and
(2+5x4=) 22.
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Stratified sampling
A two-step process:
1. The population is divided into subpopulations/strata.
➢The strata should be mutually exclusive that every
respondent should be assigned to one and only one group.
➢The elements within a stratum/group should be as
homogeneous as possible, but the elements/respondents in
different strata should be as heterogeneous as possible.
2. Elements are then selected from each group by a random
procedure, usually simple random sampling.
A graphical illustration of stratified sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Randomly select a
number from 1 to 5
for each stratum, A to E.
The resulting
sample consists of
population elements
4, 7, 13, 19 and 21. Note,
one element
is selected from each
column.
Cluster sampling
• The target population is first divided into mutually exclusive and
collectively exhaustive subpopulations, or clusters.
• Elements within a cluster should be as heterogeneous as possible,
but clusters themselves should be as homogeneous as possible.
Ideally, each cluster should be a small-scale representation of the
population.
• Then a random sample of clusters is selected, based on a
probability sampling technique such as simple random sampling.
• 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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A graphical illustration of cluster Sampling
(2-stage)
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Randomly select 3
clusters, B, D and E.
Within each cluster,
randomly select one
or two elements.
The resulting sample
consists of
population elements
7, 18, 20, 21, and 23.
Note, no elements
are selected from
clusters A and C.
http://keydifferences.com/difference-between-stratified-and-cluster-sampling.html
http://keydifferences.com/differen
ce-between-stratified-and-cluster-
sampling.html
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Internet sampling
• Online intercept sampling: Visitors to a Web site are
intercepted and given an opportunity to participate in the
survey. The interception can be made at one or more Web sites,
including high-traffic sites such as Yahoo.
• Recruited online sampling, e.g. an online panel: A group of
selected research participants who have agreed to provide
information.
Determining the sample size
Factors to be considered:
• the importance of the decision
• the nature of the research and the analysis
• the number of variables
• sample sizes used in similar studies
• completion rates
• resource constraints
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https://www.statisticssolutions.com/sample-size-formula/
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Sample sizes used in marketing
research studies
Type of Study
Minimum Size Typical Range
Problem identification research (e.g.
market potential)
500
1,000-2,500
Problem-solving research (e.g.
pricing)
200 300-500
Product tests
200 300-500
Test marketing studies
200 300-500
TV, radio, or print advertising (per
commercial or ad tested)
150 200-300
Test-market audits
10 stores 10-20 stores
Focus groups
2 groups 6-15 groups
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Ensure that the sampling strategy adopted is fully described in
research proposals and the outputs.
Also include information on the barriers and problems
encountered during sampling and how these were addressed.
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Case study (1)
• Case study is a research strategy which focuses on gaining an
understanding of the dynamics present within single settings.
• Case studies can be used when a ‘how’ or ‘why’ question is
being asked.
• The case study method can be used for a wide variety of
issues, including the evaluation of training programmes,
organizational performance, product design and its
introduction to the market.
• A case may be an individual, an organization, a role, a
community or a nation.
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Case study (2)
• While surveys tend to collect data on a limited range of topics
but from many people, case studies can explore many themes
and subjects, but from a much more focused range of people,
organizations or contexts.
• In contrast to methods such as descriptive surveys, case studies
are also trying to attribute causal relationships and are not just
describing a situation.
• The case study approach can be used as both a qualitative and
quantitative method, however, more often used as a qualitative
method.
Case study (3)
• Case study research involves a detailed examination of a small
sample of interest.
• The case study approach requires the collection of multiple
sources of data.
• Case studies typically combine data collection methods from a
wide variety of sources including archives, interviews,
surveys, visual methods and participant observation.
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Selecting cases
• While the cases may be chosen randomly, this not preferable.
• Given that the number of cases studied is usually quite limited,
it makes sense to choose those that are extreme types.
• While there is no ideal number of cases, four to ten cases
usually works well.
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Limitations of case studies
• Amount of time they take and the volume of documentation
they generate.
• Tend to be context-specific.
• Illuminating rather than confirmatory.
• Limits on generalisability.
Main data collection sources
• Documentation
• Observation
• Interview
Next Topic: AMR5 – Interviewing
• Different types of interviews
• Conducting the interview
• Analysing interview data
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