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Determining How toSelect a Sample
Edited & Complied
By
Sanjeev S. MalageAssociate Professor
FMS Department , NIFT, Bangalore
Sanjeev Sadashiv Malage NIFT, Bangalore2
Learning Objectives
1. To understand the concept of sampling.
2. To learn the steps in developing a sampling plan.
3. To understand the concepts of sampling error andnonsampling error.
4. To distinguish between probability samples, andnonprobability samples.
5. To understand sampling implications of surveying over theInternet.
Sanjeev Sadashiv Malage NIFT, Bangalore3
Definition of sampling
Procedure by which some membersof a given population are selected as
representatives of the entire population
Sanjeev Sadashiv Malage NIFT, Bangalore4
Ø Sampling Defined:The process of obtaining informationfrom a subset of a larger group.
Ø A market researcher takes the results from the sample tomake estimates of the larger group.
Ø Sampling a small percentage of a population can result invery accurate estimates.
To understand theconcept of sampling.
The Concept of Sampling
Sanjeev Sadashiv Malage NIFT, Bangalore5
Why do we use samples ?
Get information from large populations– At minimal cost
– At maximum speed
– At increased accuracy
– Using enhanced tools
Sanjeev Sadashiv Malage NIFT, Bangalore6
What we need to know
• Concepts– Representativeness
– Sampling methods
– Choice of the right design
Sanjeev Sadashiv Malage NIFT, Bangalore7
Sampling and representativeness
Sample
Target Population
SamplingPopulation
Target Populationè Sampling Populationè Sample
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Step1.Define the
Population ofInterest
Step 2. ChooseData Collection
Method
Step 3.Choose Sampling
Frame
(4)Select a
Sampling Method
Step 5. DetermineSample Size
Step 6. DevelopOperational Plan
Step 7.Execute
Operational Plan
Steps in Developing a Sample Plan
Sanjeev Sadashiv Malage NIFT, Bangalore9
Step One: Defining the Population of InterestSpecifying the characteristics from whom information isneeded.Define the characteristics of those that should beexcluded.
Step Two: Choose Data Collection MethodImpacts for the sampling process.
Step Three: Choosing Sampling FrameA list of elements or members from which we select unitsto be sampled.
To learn the steps indeveloping a sample plan.
Steps In Developing ASampling Plan
Sanjeev Sadashiv Malage, NIFT, Bangalore10
Basic Concepts in Sampling
• Population: the entire group under studyas defined by research objectives– Researchers define populations in
specific terms such as “heads ofhouseholds located in areas servedby the company who are responsiblefor making the decision.”
Sanjeev Sadashiv Malage, NIFT, Bangalore11
Basic Concepts in Sampling
• Sample: a subset of the populationthat should represent the entire group
• Sample unit: the basic level ofinvestigation
• Census: an accounting of thecomplete population
Sanjeev Sadashiv Malage, NIFT, Bangalore12
Step Four: Select a Sampling MethodThe selection will depend on:
• The objectives of the study
• The financial resources available
• Time limitations
• The nature of the problem
To learn the steps indeveloping a sample plan.
Steps In Developing ASampling Plan
Sanjeev Sadashiv Malage, NIFT, Bangalore13
Step Five: Determine Sample Size• Available budget
• Rules of thumb
Step Six: Develop of Operational Procedures forSelecting Sample Elements
Specify whether a probability or nonprobabilitysample is being used
Step Seven: Execution the Sampling Plan
The final step of the operational sampling plan
Include adequate checking of specified procedures.
Steps In Developing A Sampling Plan
Sanjeev Sadashiv Malage, NIFT, Bangalore14
Samplingmethods
Probabilitysamples
Systematic
Cluster Stratified
Simplerandom
Nonprobabilitysamples
Convenience
Judgement
Snowball
Quota
Classification of Sampling Methods
Sanjeev Sadashiv Malage, NIFT, Bangalore15
Two Basic Sampling Methods
• Probability samples: ones in whichmembers of the population have aknown chance (probability) of beingselected into the sample
• Non-probability samples: instances inwhich the chances (probability) ofselecting members from thepopulation into the sample areunknown
Sanjeev Sadashiv Malage, NIFT, Bangalore16
Probability Sampling:Simple Random Sampling
• Simple random sampling:
• the probability of being selected intothe sample is “known” and equal forall members of the population– E.g., Blind Draw Method– Random Numbers Method
Sanjeev Sadashiv Malage, NIFT, Bangalore17
Simple random sampling
• Principle–Equal chance of drawing each unit
• Procedure–Number all units–Randomly draw units
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Probability Sampling:1.Simple Random Sampling
– Advantage:• Known and equal chance of selection
– Disadvantages:• Complete accounting of population
needed• Cumbersome to provide unique
designations to every populationmember
Sanjeev Sadashiv Malage, NIFT, Bangalore19
Example: evaluate the prevalence of toothdecay among the 1200 children attendinga school
• List of children attending the school• Children numerated from 1 to 1200• Sample size = 100 children• Random sampling of 100 numbers
between 1 and 1200How to randomly select?
Simple random sampling
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Simple random sampling
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Probability SamplingSystematic Sampling
• Systematic sampling: way to select arandom sample from a directory orlist that is much more efficient thansimple random sampling– Skip interval=population list
size/sample size
Sanjeev Sadashiv Malage, NIFT, Bangalore22
Systematic sampling
• N = 1200, and n = 60Þ sampling fraction = 1200/60 = 20
• List persons from 1 to 1200• Randomly select a number between 1
and 20 (ex : 8)Þ 1st person selected = the 8th on
the listÞ 2nd person = 8 + 20 = the 28th
etc .....
Sanjeev Sadashiv Malage, NIFT, Bangalore23
Systematic sampling
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
46 47 48 49 50 51 52 53 54 55 ……..
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Systematic sampling
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Probability SamplingSystematic Sampling
– Advantages:• Approximate known and equal
chance of selection…it is a probabilitysample plan
• Efficiency…do not need to designateevery population member
• Less expensive…faster than SRS– Disadvantage:
• Small loss in sampling precision
Sanjeev Sadashiv Malage, NIFT, Bangalore27
Probability SamplingCluster Sampling
• Cluster sampling: method inwhich the population is dividedinto groups, any of which can beconsidered a representativesample– Area sampling
Sanjeev Sadashiv Malage, NIFT, Bangalore28
Cluster sampling
• Principle
– Random sample of groups (“clusters”)of units
– In selected clusters, all units orproportion (sample) of units included
Sanjeev Sadashiv Malage, NIFT, Bangalore29
Cluster Sampling
• In cluster sampling the population isdivided into subgroups, called“clusters.”
• Each cluster should represent thepopulation.
• Area sampling is a form of clustersampling – the geographic area isdivided into clusters.
Sanjeev Sadashiv Malage, NIFT, Bangalore30
Cluster Sampling
• One cluster may be selected torepresent the entire area with theone-step area sample.
• Several clusters may be selectedusing the two-step area sample.
Sanjeev Sadashiv Malage, NIFT, Bangalore31
A Two-Step Cluster Sample
• A two-step cluster sample (samplingseveral clusters) is preferable to aone-step (selecting only one cluster)sample unless the clusters arehomogeneous.
Sanjeev Sadashiv Malage, NIFT, Bangalore32
Example: Cluster sampling
Section 4
Section 5
Section 3
Section 2Section 1
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cluster sampling
To evaluate vaccination coverage:
• Without list of persons
• Total population of villages
• Randomly choose 30 clusters
• 30 cluster of 7 children each= 210 children
Sanjeev Sadashiv Malage, NIFT, Bangalore34
Probability SamplingCluster Sampling
– Advantage:• Economic efficiency…faster and
less expensive than SRS– Disadvantage:
• Cluster specification error…themore homogeneous the clusters,the more precise the sampleresults
Sanjeev Sadashiv Malage, NIFT, Bangalore35
Stratified Sampling
• When the researcher knows theanswers to the research question arelikely to vary by subgroups…
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Probability SamplingStratified Sampling
• Stratified sampling: method in whichthe population is separated intodifferent strata and a sample is takenfrom each stratum– Proportionate stratified sample– Disproportionate stratified sample
Sanjeev Sadashiv Malage, NIFT, Bangalore37
Stratified sampling
• Principle :
–Classify population into internallyhomogeneous subgroups (strata)
–Draw sample in each strata–Combine results of all strata
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Stratified Sampling
– Research Question: “To what extentdo you value your college degree?”Answers are on a five point scale: 1=“Not valued at all” and 5= “Veryhighly valued”• We would expect the answers to vary
depending on classification. Freshersare likely to value less than Alumni. Wewould expect the mean scores to behigher as classification goes up.
Sanjeev Sadashiv Malage, NIFT, Bangalore39
Stratified Sampling
– Research Question: “To what extentdo you value your college degree?”• We would also expect there to be more
agreement (less variance) asclassification goes up. That is, seniorsshould pretty much agree that there isvalue. Freshers will have lessagreement.
Sanjeev Sadashiv Malage, NIFT, Bangalore40
Stratified Sampling
• Why is stratified sampling moreaccurate when there are skewedpopulations?– The less variance in a group, the less
sample size it takes to produce aprecise answer.
– Why? If 99% of the population (lowvariance) agreed on the choice of BrandA, it would be easy to make a preciseestimate that the population preferredBrand A even with a small sample size.
Sanjeev Sadashiv Malage, NIFT, Bangalore41
Stratified Sampling
– But, if 33% chose Brand A, and 23%chose B, and so on (high variance) itwould be difficult to make a preciseestimate of the population’s preferredbrand…it would take a larger samplesize…
Sanjeev Sadashiv Malage, NIFT, Bangalore42
Stratified Sampling
– Stratified sampling allows theresearcher to allocate more sample sizeto strata with less variance and lesssample size to strata with less variance.Thus, for the same sample size, moreprecision is achieved.
– This is normally accomplished bydisproportionate sampling. Seniorswould be sampled LESS than theirproportionate share of the populationand freshmen would be sampled more.
Sanjeev Sadashiv Malage, NIFT, Bangalore43
Probability SamplingStratified Sampling
– Advantage:• More accurate overall sample of
skewed population…see next slidefor WHY
– Disadvantage:• More complex sampling plan
requiring different sample size foreach stratum
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Nonprobability Sampling
• With nonprobability samplingmethods selection is not based onfairness, equity, or equal chance.– Convenience sampling– Judgment sampling– Referral sampling– Quota sampling
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Nonprobability Sampling
• May not be representative but theyare still used very often. Why?– Decision makers want fast,
relatively inexpensive answers…nonprobability samples are fasterand less costly than probabilitysamples.
Sanjeev Sadashiv Malage, NIFT, Bangalore46
Nonprobability Sampling
• May not be representative but theyare still used very often. Why?– Decision makers can make a
decision based upon what 100 or200 or 300 people say…they don’tfeel they need a probability sample.
Sanjeev Sadashiv Malage, NIFT, Bangalore47
Nonprobability Sampling
• Convenience samples: samplesdrawn at the convenience of theinterviewer– Error occurs in the form of
members of the population who areinfrequent or nonusers of thatlocation
Sanjeev Sadashiv Malage, NIFT, Bangalore48
Nonprobability Sampling
• Judgment samples: samples thatrequire a judgment or an “educatedguess” as to who should representthe population– Subjectivity enters in here, and
certain members will have asmaller chance of selection thanothers
Sanjeev Sadashiv Malage, NIFT, Bangalore49
Nonprobability Sampling
• Referral samples (snowball samples):(snowball samples):samples which require respondentsto provide the names of additionalrespondents– Members of the population who are
less known, disliked, or whoseopinions conflict with therespondent have a low probabilityof being selected
Sanjeev Sadashiv Malage, NIFT, Bangalore50
Nonprobability Sampling
• Quota samples: samples that use aspecific quota of certain types ofindividuals to be interviewed– Often used to ensure that
convenience samples will havedesired proportion of differentrespondent classes
Sanjeev Sadashiv Malage, NIFT, Bangalore51
Online Sampling Techniques
• Random online intercept sampling:relies on a random selection of Website visitors
• Invitation online sampling: is whenpotential respondents are alerted thatthey may fill out a questionnaire thatis hosted at a specific Web site
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Online Sampling Techniques
• Online panel sampling: refers toconsumer or other respondent panelsthat are set up by marketing researchcompanies for the explicit purpose ofconducting surveys withrepresentative samples
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Basic Concepts in Sampling
• Sampling error: any error in a surveythat occurs because a sample is used
• A sample frame: a master list of theentire population
• Sample frame error: the degree towhich the sample frame fails toaccount for all of the population…atelephone book listing does notcontain unlisted numbers