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8/12/2019 Marketing Research Ch 11
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Chapter Eleven
Sampling:
Design and Procedures
2007 Prentice Hall 11-1
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SAMPLE OR CENSUS
Population: A population is the entire group we areinterested in, which we wish to describe or drawconclusions about. For example, the population for astudy of infant health might be all children born inBangladesh in the 2006's.
A census involves a complete enumeration of the
elements of a population.
Sample: A sample is a group of units selected from alarger group (the population). For example, the samplemight be all babies born on 7th May in the 1980's.
Sample characteristics, called statistics used to makeinferences about population parameters.
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The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
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Define the Target Population
The target population is the collection of elements orobjects that possess the information sought by theresearcher and about which inferences are to be made.The target population should be defined in terms ofelements, sampling units, extent, and time.
An elementis the object about which or from whichthe information is desired, e.g., male or female headof the household responsible for most of the shoppingat department stores.
A sampling unitis an element, or a unit containing
the element, that is available for selection at somestage of the sampling process. e.g., households Extentrefers to the geographical boundaries.
Metropolitan city Timeis the time period under consideration. 2006
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Determine the sampling frame
A sampling frame consists of a list or set ofdirections for identifying the target population.E.g. telephone book
Select a sampling techniqueThe researcher must decide whether to use
nonprobability or probability sampling.
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Determine the Sample Size
Sample size refers to the number of elements tobe included in the study.
Qualitative Factors to be considered: The importance of the decision: more important, more larger
The nature of the research: exploratorysmall, conclusivelarge
The number of variables: manylarge
The nature of analysis: sophisticated analysis of the datausing multivariate techniquelarge
Sample sizes used in similar studiesrefer to next slide
Resource constraints: limited money and time - small
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Sample Sizes Used in MarketingResearch 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 (percommercial 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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Classification of Sampling Techniques
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
ConvenienceSampling
JudgmentalSampling
QuotaSampling
SnowballSampling
SystematicSampling
StratifiedSampling
ClusterSampling
Other SamplingTechniques
Simple RandomSampling
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Nonprobability Sampling
Nonprobability sampling relies on the personaljudgment of the researcher rather than chanceto select sample elements.
Probability Sampling
Sampling units are selected by chance.
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Convenience Sampling
Convenience samplingattempts to obtain asample of convenient elements. Often, respondentsare selected because they happen to be in the rightplace at the right time.
use of students, and members of socialorganizations
mall intercept interviews without qualifying therespondents
department stores using charge account lists
people on the street interviews
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A Graphical Illustration ofConvenience 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 theelements in this
Group are selected.
The resulting sample
consists of elements
16, 17, 18, 19 and 20.
Note, no elements are
selected from group
A, B, C and E.
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Judgmental Sampling
Judgmental samplingis a form ofconvenience sampling in which thepopulation elements are selectedbased on the judgment of theresearcher.
purchase engineers selected inindustrial marketing research
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Graphical Illustration of JudgmentalSampling
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 Eto betypical and convenient.
Within each of thesegroups one or two
elements are selectedbased on typicality and
convenience. The
resulting sampleconsists of elements 8,
10, 11,13, and 24. Note,no elements are selected
from groups A and D.
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Quota Sampling
Quota samplingmay be viewed as two-stage restricted judgmentalsampling.
The first stage consists of developing control categories, or quotas,of population elements.
In the second stage, sample elements are selected based onconvenience or judgment.
Population Samplecomposition composition
Control
Characteristic Percentage Percentage NumberSexMale 48 48 480Female 52 52 520
____ ____ ____100 100 1000
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A Graphical Illustration ofQuota 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, oneelement isselected based on
judgment or
convenience. The
resulting sample
consistsof elements3, 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 ofrespondents is selected, usually at random.
After being interviewed, these respondents areasked to identify others who belong to the targetpopulation of interest.
Subsequent respondents are selected based onthe referrals.
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A Graphical Illustration ofSnowball 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 referselements 12 and 13.
Element 9 refers
element 18. The
resulting sample
consistsof elements2, 9, 12, 13, and 18.
Note, there are no
element from group E.
Random
Selection Referrals
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Simple Random Sampling
Each element in the population hasan equal probability of selection.
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A Graphical Illustration ofSimple 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. Theresulting sample
consists of
population
elements 3, 7, 9,
16, and 24. Note,there is no
element from
Group C.
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Systematic Sampling
The sample is chosen by selecting a randomstarting point and then picking every ithelement in succession from the sampling
frame.
The sampling interval, i, is determined bydividing the population size N by the sample
size n and rounding to the nearest integer.
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Systematic Sampling
If there are 100,000 elements in thepopulation and a sample of 1,000 is desired.In this case the sampling interval, i, is 100.
A random number between 1 and 100 isselected. If, for example, this number is 23,the sample consists of elements 23, 123,
223, 323, 423, 523, and so on.
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A Graphical Illustration ofSystematic 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 to
5, say 2.The resulting sample
consists of
population 2,
(2+5=) 7, (2+5x2=) 12,
(2+5x3=)17, and
(2+5x4=) 22.Note, allthe elements are
selected from a
single row.
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Stratified Sampling
A two-step process in which the population ispartitioned into subpopulations, or strata.
Next, elements are selected from eachstratum by a random procedure, usually SRS.
The elements within a stratum should be as
homogeneous as possible, but the elements indifferent strata should be as heterogeneousas possible.
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A Graphical Illustration ofStratified 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 toE. The resultingsample consists of
population elements4, 7, 13, 19 and 21.
Note, one elementis selected from each
column.
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Cluster Sampling
The target population is first divided into mutuallyexclusive subpopulations, or clusters.
Then a random sample of clusters is selected, basedon a probability sampling technique such as SRS.
For each selected cluster, either all the elements are
included in the sample (one-stage) or a sample ofelements is drawn probabilistically (two-stage).
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Cluster Sampling
Elements within a cluster should be asheterogeneous as possible, but
clusters themselves should be ashomogeneous as possible.
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A Graphical Illustration ofCluster 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 oneor 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.
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Types of Cluster Sampling
Cluster Sampling
One-StageSampling
MultistageSampling
Two-StageSampling
Simple ClusterSampling
ProbabilityProportionate
to Size Sampling
Strengths and Weaknesses of
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Technique Strengths WeaknessesNonprobability Sampling
Convenience samplingLeast expensive, leasttime-consuming, mostconvenient
Selection bias, sample notrepresentative, not recommended fordescriptive or causal research
Judgmental sampling Low cost, convenient,not time-consuming
Does not allow generalization,subjective
Quota sampling Sample can be controlledfor certain characteristics
Selection bias, no assurance ofrepresentativeness
Snowball sampling Can estimate rarecharacteristics
Time-consuming
Probability samplingSimple random sampling(SRS)
Easily understood,results projectable
Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.
Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary
Can decrease representativeness
Stratified sampling Include all importantsubpopulations,
precision
Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive
Cluster sampling Easy to implement, costeffective
Imprecise, difficult to compute andinterpret results
Strengths and Weaknesses ofBasic Sampling Techniques
Choosing Nonprobability Vs
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Choosing Nonprobability Vs.Probability Sampling
Conditions Favoring the Use ofFactors Nonprobability
samplingProbabilitysampling
Nature of research Exploratory Conclusive
Relative magnitude of samplingand nonsampling errors
Nonsamplingerrors arelarger
Samplingerrors arelarger
Variability in the population Homogeneous
(low)
Heterogeneou
s (high)
Statistical considerations Unfavorable Favorable
Operational considerations Favorable Unfavorable
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