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8/2/2019 Session 6 - Sampling Technique
1/23
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11
Session
6Sampling techniques
8/2/2019 Session 6 - Sampling Technique
2/23
Research Proposal Assign.given in last class
Executive SummaryBackground Problem Definition/Objectives of the
ResearchApproach to the Problem (Theoretical
background with references, RQs andHypotheses)
Research Design Fieldwork/Data CollectionData AnalysisReporting (Contents, etc) PERT/CPM chartAppendices (Relevant textbook material,
To be submitted & presented in thenext class
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Sample vs. Census
Cond i t io n s F a v o r i n g t h e U s e o fT ype o f S t ud y S am p l e Cen su s
1 . Bud ge t Sma l l L a r g e2 . Tim e av a i l ab l e Sho r t L ong3 . Po pu l a t i on s i z e L a r g e Sma l l4 . Va r i ance i n t he cha rac t e r i s t ic Sma l l L a r g e5 . Co s t o f s am p l i ng e r r o r s L o w H i g h6 . Cos t o f nonsam p l i ng e r r o r s H i g h L ow7 . Na t u r e o f mea su r em en t Des t ruc t i v e Nondes t ruc t i v e8 . A t t en t i on t o i nd i v i dua l ca ses Y e s N o 33
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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
or objects that possess the information sought by the
researcher and about which inferences are to be
made. The target population should be defined in
terms of elements, sampling units, extent, and time.
An element is the object about which or from
which the information is desired, e.g., the
respondent.A sampling unit is an element, or a unit
containing the element, that is available for
selection at some stage of the sampling process.
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Classification of SamplingTechniques
Sampling Techniques
NonprobabilitySampling Techniques ProbabilitySampling
Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systemat
icSampling
Stratifie
dSampli
Cluster
Sampling
OtherSampling
Techniques
Simple
Random 66
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Convenience Sampling
Convenience sampling attempts to obtain asample of convenient elements. Often, respondentsare selected because they happen to be in the right
place 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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Judgmental Sampling
Judgmental sampling is 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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Quota Sampling
Quota sampling may be viewed as two-stage restrictedjudgmental sampling.
The first stage consists of developing control categories, orquotas, of population elements.
In the second stage, sample elements are selected based on
convenience or judgment.
Population Samplecomposition composition
Control
Characteristic Percentage Percentage NumberSexMale 48 48 480Female 52 52 520
____ ____ ____ 100 100 1000
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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 on
the referrals.
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Simple Random Sampling - SRS
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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Systematic Sampling
The sample is chosen by selecting a random starting pointand then picking every ith element in succession from thesampling frame.
The sampling interval, i, is determined by dividing thepopulation size N by the sample size n and rounding to the
nearest integer.When the ordering of the elements is related to the
characteristic of interest, systematic sampling increases therepresentativeness of the sample.
If the ordering of the elements produces a cyclical pattern,
systematic sampling may decrease the representativenessof the sample.
For example, there are 100,000 elements in the populationand a sample of 1,000 is desired. In this case the samplinginterval, i, is 100. A random number between 1 and 100 is
selected. If, for example, this number is 23, the sampleconsists of elements 23 123 223 323 423 523 and so on.
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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 populationelement should be assigned to one and only onestratum and no population elements should beomitted.
Next, elements are selected from each stratum bya random procedure, usually SRS.
A major objective of stratified sampling is toincrease precision without increasing cost.
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Cluster Sampling
The target population is first divided into mutually exclusiveand collectively exhaustive subpopulations, or clusters.
Then a random sample of clusters is selected, based on aprobability sampling technique such as SRS.
For each selected cluster, either all the elements areincluded in the sample (one-stage) or a sample of elementsis drawn probabilistically (two-stage).
Elements within a cluster should be as heterogeneous aspossible, but clusters themselves should be ashomogeneous as possible. Ideally, each cluster should be asmall-scale representation of the population.
In probability proportionate to sizesampling, theclusters are sampled with probability proportional to size. Inthe second stage, the probability of selecting a sampling
unit in a selected cluster varies inversely with the size of the1414
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Types of Cluster Sampling
Cluster Sampling
One-StageSampling
MultistageSampling
Two-StageSampling
Simple ClusterSampling
ProbabilityProportionate
to Size Sampling
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Techniq
ue
Strengt
hs
Weaknes
sesNonprobability
SamplingConvenience
sampling
Least expensive,
leasttime-consuming,
mostconvenie
nt
Selection bias, sample
notrepresentative, not recommended
fordescriptive or causal
researchJudgmental
sampling
Low cost,
convenient,not time-
consuming
Does not allow
generalization,subjecti
veQuota
sampling
Sample can be
controlledfor certaincharacteristics
Selection bias, no assurance
ofrepresentativenessSnowball
sampling
Can estimate
rarecharacteristi
cs
Time-
consuming
Probability
samplingSimple random
sampling(S
RS)
Easily
understood,resul
ts
projecta
ble
Difficult to construct
samplingframe,
expensive,
lower
precision,no assurance
of
representativen
ess.
Systematic
sampling
Can
increaserepresentativen
ess,easier to implement
thanSRS, sampling frame
notnecessa
ry
Can
decrease
representativen
ess
Stratified
sampling
Include all
importantsubpopulatio
ns,precisi
on
Difficult to select
relevantstratification variables, not
feasible tostratify on many variables,
expensiveCluster
sampling
Easy to implement,
costeffecti
ve
Imprecise, difficult to compute
andinterpret
results
Strengths and Weaknesses ofBasic Sampling Techniques
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Procedures for DrawingProbability Samples
SimpleRandomSampling
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 randomnumbers
between 1 and N
4. The numbers generated denote the elements thatshould be included in the sample
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Procedures for Drawing
Probability Samples
SystematicSampling
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 afraction,
round to the nearest integer
4. Select a random number, r, between 1 and i, asexplained 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+1818
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1. Select a suitable frame
2. Select the stratification variable(s) and the number ofstrata, 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
Procedures for Drawing
Probability Samples
nh =
nh=1
H
StratifiedSampling
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Procedures for Drawing
Probability Samples
ClusterSampling
1. Assign a number from 1 to N to each element in thepopulation2. Divide the population into C clusters of which c will beincluded in
the sample3. Calculate the sampling interval i, i=N/c (round to nearestinteger)4. Select a random number r between 1 and i, as explained insimple
random 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 onSRS
or systematic sampling8. Remove clusters exceeding sampling interval i. Calculate2020
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Procedures for DrawingProbability Samples
Repeat the process until each of the remainingclusters has a population less than the
sampling interval. If b clusters have beenselected with certainty, select the remainingc-b clusters according to steps 1 through 7.The fraction of units to be sampled withcertainty is the overall sampling fraction = n/N.Thus, for clusters selected with certainty, we
would select ns=(n/N)(N1+N2+...+Nb) units. Theunits selected from clusters selected underPPS sampling will therefore be n*=n- ns.
ClusterSampling
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Choosing Nonprobability vs.Probability Sampling
C on d i t io n s F a v o r i n g t h e U s e o fFa c t o r s Nonp robab i l i t ys a m p l i n g P r obab i l i t ys a m p l i n gN a tu r e o f r e s ea r ch Exp l o r a t o r y Conc l u s i v e
R e l a t iv e m a gn i t u de o f s a m p l i n ga n d n on s a m p l in g e r r o rs N o n s a m p l i n ge r r o r s a r el a r ge rS a m p l i n ge r ro r s a rel a r ge r
Va r i a b i l it y i n t h e pop u l a t i on H o m o g e n e o u s( l o w ) He t e r oge n e ou s( h i g h )S t a t i s ti c a l c o n s i de r a t i on s Un f a vo r ab l e Fa vo r ab l e
Ope r a t i ona l con s i de r a t i on s Favo r ab l e Un f a vo r ab l e2222
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Assignment Data collection process and field work
Quiz from sampling