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Chapter Eleven
Sampling:
Design and Procedures
2007 Prentice Hall 11-1
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2007 Prentice Hall 11-2
Chapter Outline
1) Overview
2) Sample or Census
3) The Sampling Design Process
i. Define the Target Population
ii. Determine the Sampling Frame
iii. Select a Sampling Technique
iv. Determine the Sample Size
v. Execute the Sampling Process
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2007 Prentice Hall 11-3
Chapter Outline
4) A Classification of Sampling Techniques
i. Nonprobability Sampling Techniques
a. Convenience Sampling
b. Judgmental Sampling
c. Quota Samplingd. Snowball Sampling
ii. Probability Sampling Techniques
a. Simple Random Sampling
b. Systematic Sampling
c. Stratified Sampling
d. Cluster Sampling
e. Other Probability Sampling Techniques
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2007 Prentice Hall 11-4
Chapter Outline
5. Choosing Nonprobability Versus ProbabilitySampling
6. Uses of Nonprobability Versus ProbabilitySampling
7. Internet Sampling
8. International Marketing Research
9. Ethics in Marketing Research
10. Summary
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Sample Vs. CensusTable 11.1
Conditions Favoring the Use ofType 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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The Sampling Design Process
Fig. 11.1
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 element is the object about which or from whichthe information is desired, e.g., the respondent.
Asampling unit is an element, or a unit containing
the element, that is available for selection at somestage of the sampling process.
Extent refers to the geographical boundaries.
Time is the time period under consideration.
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Define the Target Population
Important qualitative factors in determining thesample size are:
the importance of the decision
the nature of the research
the number of variables
the nature of the analysis
sample sizes used in similar studies incidence rates
completion rates
resource constraints
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Sample Sizes Used in MarketingResearch Studies
Table 11.2
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
Fig. 11.2
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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 SamplingFig. 11.3
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 of conveniencesampling in which the population elements areselected based on the judgment of the researcher.
test markets
purchase engineers selected in industrialmarketing research
bellwether precincts selected in voting behaviorresearch
expert witnesses used in court
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Graphical Illustration of JudgmentalSampling
Fig. 11.3
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
Fig. 11.3
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 has a known andequal probability of selection.
Each possible sample of a given size (n) has aknown and equal probability of being the sampleactually selected.
This implies that every element is selectedindependently of every other element.
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A Graphical Illustration ofSimple Random Sampling
Fig. 11.4
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 random startingpoint and then picking every ith element insuccession from the sampling frame.
The sampling interval, i, is determined by dividing thepopulation size N by the sample size n and roundingto the nearest integer.
When the ordering of the elements is related to thecharacteristic of interest, systematic samplingincreases the representativeness of the sample.
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Systematic Sampling
If the ordering of the elements produces a cyclicalpattern, systematic sampling may decrease therepresentativeness of the sample.
For example, there are 100,000 elements in thepopulation and a sample of 1,000 is desired. In thiscase the sampling interval, i, is 100. A randomnumber between 1 and 100 is selected. 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 SamplingFig. 11.4
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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2007 Prentice Hall 11-24
Stratified Sampling
A two-step process in which the population ispartitioned into subpopulations, or strata.
The strata should be mutually exclusive and collectively
exhaustive in that every population element should beassigned to one and only one stratum and no populationelements should be omitted.
Next, elements are selected from each stratum by arandom procedure, usually SRS.
A major objective of stratified sampling is to increaseprecision without increasing cost.
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Stratified Sampling
The elements within a stratum should be ashomogeneous as possible, but the elements indifferent strata should be as heterogeneous aspossible.
The stratification variables should also be closelyrelated to the characteristic of interest.
Finally, the variables should decrease the cost ofthe stratification process by being easy to measureand apply.
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Stratified Sampling
In proportionate stratified sampling, the size ofthe sample drawn from each stratum is proportionateto the relative size of that stratum in the total
population.
In disproportionate stratified sampling, the sizeof the sample from each stratum is proportionate to
the relative size of that stratum and to the standarddeviation of the distribution of the characteristic ofinterest among all the elements in that stratum.
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A Graphical Illustration ofStratified SamplingFig. 11.4
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 and collectively exhaustive 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 areincluded 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 themselvesshould be as homogeneous as possible. Ideally,
each cluster should be a small-scale representationof the population.
In probability proportionate to sizesampling,the clusters are sampled with probability
proportional to size. In the second stage, theprobability of selecting a sampling unit in a selectedcluster varies inversely with the size of the cluster.
G hi l ll i f
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A Graphical Illustration ofCluster Sampling (2-Stage)Fig. 11.4
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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2007 Prentice Hall 11-31
Types of Cluster Sampling
Fig 11.5
Cluster Sampling
One-StageSampling
MultistageSampling
Two-StageSampling
Simple ClusterSampling
ProbabilityProportionate
to Size Sampling
Strengths and Weaknesses of
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Technique Strengths WeaknessesNonprobability SamplingConvenience sampling
Least 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
Table 11.3
Strengths and Weaknesses ofBasic Sampling Techniques
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A Classification of Internet Sampling
Fig. 11.6
Internet Sampling
Online InterceptSampling Recruited OnlineSampling Other Techniques
Nonrandom Random Panel Nonpanel
RecruitedPanels
Opt-inPanels
Opt-in ListRentals
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Procedures for DrawingProbability Samples
Exhibit 11.1
Simple Random
Sampling
1. Select a suitable sampling frame
2. Each element is assigned a number from 1 to N(pop. size)
3. Generate n (sample size) different random numbersbetween 1 and N
4. The numbers generated denote the elements thatshould be included in the sample
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Procedures for DrawingProbability Samples
Exhibit 11.1, cont.Systematic
Sampling
1.Select a suitable sampling frame
2. Each element is assigned a number from 1 to N (pop. size)3. Determine the sampling interval i:i=N/n. If i is a fraction,
round to the nearest integer
4. Select a random number, r, between 1 and i, as explained in
simple random sampling
5. The elements with the following numbers will comprise thesystematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i
Procedures for Drawing
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Procedures for DrawingProbability Samples
1. Select a suitable frame
2. Select the stratification variable(s) and the number of strata, H
3. Divide the entire population into H strata. Based on theclassification variable, each element of the population is assignedto one of the H strata
4. In each stratum, number the elements from 1 to Nh (the pop.size of stratum h)
5. Determine the sample size of each stratum, nh, based onproportionate or disproportionate stratified sampling, where
6. In each stratum, select a simple random sample of size nh
Exhibit 11.1, cont.
nh = nh=1
H
Stratified
Sampling
P oced es fo D a ing
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Procedures for DrawingProbability Samples
Exhibit 11.1, cont.
Cluster
Sampling
1. Assign a number from 1 to N to each element in the population
2. Divide the population into C clusters of which c will be included inthe sample
3. Calculate the sampling interval i, i=N/c (round to nearest integer)
4. Select a random number r between 1 and i, as explained in simplerandom sampling
5. Identify elements with the following numbers:r,r+i,r+2i,... r+(c-1)i
6. Select the clusters that contain the identified elements7. Select sampling units within each selected cluster based on SRS
or systematic sampling
8. Remove clusters exceeding sampling interval i. Calculate newpopulation size N*, number of clusters to be selected C*= C-1,and new sampling interval i*.
Procedures for Drawing
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Procedures for DrawingProbability Samples
Repeat the process until each of the remaining
clusters has a population less than the sampling
interval. If b clusters have been selected withcertainty, select the remaining c-b clusters
according to steps 1 through 7. The fraction of units
to be sampled with certainty is the overall sampling
fraction = n/N. Thus, for clusters selected with
certainty, we would select ns=(n/N)(N1+N2+...+Nb)
units. The units selected from clusters selected
under two-stage sampling will therefore be n*=n- ns.
Cluster
SamplingExhibit 11.1,cont.
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)
Heterogeneous (high)
Statistical considerations Unfavorable Favorable
Operational considerations Favorable Unfavorable
Table 11.4
Tennis' Systematic Sampling
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Tennis Systematic SamplingReturns a Smash
Tennis magazine conducted a mail survey of its subscribers togain a better understanding of its market. Systematic samplingwas employed to select a sample of 1,472 subscribers from thepublication's domestic circulation list. If we assume that thesubscriber list had 1,472,000 names, the sampling intervalwould be 1,000 (1,472,000/1,472). A number from 1 to 1,000
was drawn at random. Beginning with that number, every1,000th subscriber was selected.
A brand-new dollar bill was included with the questionnaire asan incentive to respondents. An alert postcard was mailed oneweek before the survey. A second, follow-up, questionnaire
was sent to the whole sample ten days after the initialquestionnaire. There were 76 post office returns, so the neteffective mailing was 1,396. Six weeks after the first mailing,778 completed questionnaires were returned, yielding aresponse rate of 56%.